{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15 - Synthetic Control\n",
    "\n",
    "## One Amazing Math Trick to Learn What can’t be Known\n",
    "\n",
    "When we looked at difference-in-difference, we had data on multiple customers from 2 different cities: Porto Alegre and Florianopolis. The data span 2 different time periods: before and after a marketing intervention was done in Porto Alegre to boost customer deposits. To estimate the treatment effect, we ran a regression that gave us the difference-in-difference estimator and its standard error. \n",
    "\n",
    "For that case, we had a lot of samples, because data was disaggregated. But what if all we have is aggregated data on the city level? For instance, let's pretend all we have is the average level of deposits in both cities before and after the intervention.\n",
    "\n",
    "|city|before|after|\n",
    "|--|--|--|\n",
    "|FL|171.64|206.16|\n",
    "|POA|46.01|87.06|\n",
    "\n",
    "We would still be able to compute the Diff-in-Diff estimator \n",
    "\n",
    "$\n",
    "(E[Y(1)|D=1] - E[Y(1)|D=0]) - (E[Y(0)|D=1] - E[Y(0)|D=0]) = (87.06 - 206.16) - (46.01 - 171.64) = 6.53\n",
    "$\n",
    "\n",
    "However, note that the sample size here is 4, which is also the number of parameters in our Diff-in-Diff models. In this case, the standard error is not well defined, so what should we do? Another problem is that Florianopolis might not be as similar to Porto Alegre as we would want to. For instance, Florianopolis is known for its beautiful beaches and easy going people while Porto Alegre is more famous for its barbecue and prairies. The problem here is that you can't ever know for sure if you are using an appropriate control group. \n",
    "\n",
    "To work around this, we will use what is known as [**\"the most important innovation in the policy evaluation literature in the last few years\"**](https://www.aeaweb.org/articles?id=10.1257/jep.31.2.3), Synthetic Controls. It is based on a simple, yet powerful idea. We don't need to find any single unit in the untreated that is very similar to the treated. Instead, we can forge our own as a combination of multiple untreated units, creating what is effectively a synthetic control. Synthetic control is so effective yet so intuitive that it even got an article published, not on a scientific journal, but on the [Washington Post](https://www.washingtonpost.com/news/wonk/wp/2015/10/30/how-to-measure-things-in-a-world-of-competing-claims/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import style\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 6)\n",
    "style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see it in action, consider the problem of estimating the effect of cigarette taxation on its consumption. To give a bit of context, this is a question that had been debated for a long time in economics. One side of the argument says that taxes will increase the cost of cigars, which will lower its demand. The other side argues that since cigarettes cause addiction, change in their price won't change their demand by much. In economic terms, we would say that the demand for cigarettes is inelastic on price, and an increase in taxation is just a way to increase government income at the cost of smokers. To settle things, we will look at some US data regarding the matter.\n",
    "\n",
    "In 1988, California passed a famous Tobacco Tax and Health Protection Act, which became known as [Proposition 99](https://en.wikipedia.org/wiki/1988_California_Proposition_99). \"Its primary effect is to impose a 25-cent per pack state excise tax on the sale of tobacco cigarettes within California, with approximately equivalent excise taxes similarly imposed on the retail sale of other commercial tobacco products, such as cigars and chewing tobacco. Additional restrictions placed on the sale of tobacco include a ban on cigarette vending machines in public areas accessible by juveniles, and a ban on the individual sale of single cigarettes. Revenue generated by the act was earmarked for various environmental and health care programs, and anti-tobacco advertisements.\" \n",
    "\n",
    "To evaluate its effect, we can gather data on cigarette sales from multiple states and across a number of years. In our case, we got data from the year 1970 to 2000 from 39 states. Other states had similar Tobacco control programs and were dropped from the analysis. Here is what our data looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>retprice</th>\n",
       "      <th>california</th>\n",
       "      <th>after_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>3</td>\n",
       "      <td>1970</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>38.799999</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>3</td>\n",
       "      <td>1971</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>39.700001</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3</td>\n",
       "      <td>1972</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>39.900002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>3</td>\n",
       "      <td>1973</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>39.900002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3</td>\n",
       "      <td>1974</td>\n",
       "      <td>126.699997</td>\n",
       "      <td>41.900002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year     cigsale   retprice  california  after_treatment\n",
       "62      3  1970  123.000000  38.799999        True            False\n",
       "63      3  1971  121.000000  39.700001        True            False\n",
       "64      3  1972  123.500000  39.900002        True            False\n",
       "65      3  1973  124.400002  39.900002        True            False\n",
       "66      3  1974  126.699997  41.900002        True            False"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigar = (pd.read_csv(\"data/smoking.csv\")\n",
    "         .drop(columns=[\"lnincome\",\"beer\", \"age15to24\"]))\n",
    "\n",
    "cigar.query(\"california\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have `state` as the state index, where California is the number 3. Our covariates are `retprice`, the cigarette retail price, and `cigsale`, the per-capita sales of cigarettes in packs. Our outcome variable of interest is `cigsale`. Finally, we have boolean helper variables to signal the state of California and the post intervention period. If we plot the sales of cigarettes for California and other states across time, this is what we would get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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P6XGe9jX7qIoVK1KtWjVWrVpFeHg4HTp0oEGDBtSuXdvqCxfAgwcP+Oqrr9i6dSs3b94kOTnZYn9OnveMcaf79+/n1KlTVvujo6MBuHjxIhUrVqRJkyYEBgYyZ84cTpw4QZs2bWjQoAE1atTI8bCWjNiBJypHl9l7U8YX0Ny+N+X27/P8+fN89dVX7N+/n3v37pGWlmax/9E2f9K/p9dff53169czYMAAZsyYkenfUs+ePZkwYQINGjSge/fuNG7cmAYNGli9/zzKw8MDyLoHVyg8REIrFDhfX18uXryY6QdHo0aNLN5cM3vj/vDDD1mwYAH+/v60bt2agIAA8wfXb7/9lunEsIz7zYyPjw9AriahZMXNzc1qW8YHVm4nnOQ03ri4OGRZ5sGDB1ZfBv4rKSnJKjn4b2/Phg0bWLZsmcW2Pn360KlTJ+Lj4wFTSZucWLBgAePGjcPd3Z1WrVpRsmRJHBwckCSJDRs2cPbsWdLT061ul9/PlU6no0uXLhw7dozKlSvTvXt3vL29zc/V9OnTM40rMxltktkVh5zIeCwZj+2/MmuLp4k/s/NlJEonT57k5MmTWcaalJSU7WPJqad9zT5KqVSybt06Zs6cyV9//cXHH38MmKpfdO/enU8++cTc+xYXF0erVq24efMmderUoXfv3nh4eKBUKomPj2fhwoU5et4z2uvrr7/O9riMZNnV1ZXt27czffp0Nm3aZP6C4ubmxquvvsr48eNxdHR87P1mvM+lp6c/dmzxf2X23gSm9suv9yaAI0eO0KVLF/R6PS1atKB9+/a4uLigUCg4c+YMGzdutGjz3L7HZNi3bx9KpZL27dtnmswCvP322/j4+PDDDz/w/fff8+233wKmL5MTJ07MdBhcxgRLUaqx8BMJrVDgGjZsSFhYGHv27OG1117L1W2jo6P59ttvqVy5cqYTRjIbj5khY6hDZucECl1t0JzGm/F/5cqV2b9/f67v57+XrRcsWGCe/PNfGR+KOenF0uv1TJs2DT8/P3bv3m3VC3LkyJEsb5vfz9XGjRs5duwYr7zyCt98843Fvrt37z42yXpURptERkY+Uc9Zxms447H9V2Zt8TTxZzZMIaM9Bw0axIwZM3Ic+5N62tfsf7m7uzNlyhSmTJnCjRs32L9/P7/++itLly4lPDycdevWAfDLL79w8+ZNxowZYzWu9/Dhw1n2FmcV//Xr1809eI8TGBjI3LlzmTNnDpcvX2bv3r388MMPzJ8/n7i4OObPn//Yc2QkjA8ePMj0cnpByc3f56xZs0hNTWXdunVWCeOXX35pNdQrN+8xj1q3bh3du3fnlVde4ccff8xyNcdevXrRq1cvEhISOHr0KJs2beKnn36iZ8+e7N27l3Llylkcn/HlxZbtLeSMGEMrFLi+ffuiUqlYu3ZtrspZgankl9FopFWrVlbJ7O3bt7lx40aWtz116lSmky/27dsHUOgG/Oc0XmdnZypXrszly5fz/bJYhQoVcHNz48KFC48dUxYTE0N8fDz169e3SmaTkpIyvVSbISIiItOlRnPzXGX00mTW+5RRVimj9FRm95FTdevWBWD79u25ul2GjNntR44cyTTWjIlAj8rL+MH0GBQKBQcOHMjxbbJr38ftz8/XbHBwMK+88gp//fUXJUuWJCwszNzrl9FuXbp0sbpdbtotY9LYk36BrFChAm+++SabNm3Czs6O9evX5+i2GWO082rox5PKzd/ntWvX8PDwyLT3M7M2f9K/p6pVq7Jhwwa8vLzo168fa9asyfZ4V1dXQkNDmTlzJm+//TZpaWls27bN6rjLly8DPHNLChdFIqEVClxwcDBjxoxBq9Way+1kJrNxXRkzZw8ePGjxQZmUlMTIkSOtxmc+KiEhwar36ejRo6xZswZ3d3c6dOjwBI8m/+Qm3uHDh6PT6Rg2bJjVrG4wlWR6tBbjk1IqlQwcOJC0tDRGjx5tdXlWr9ebe298fHxwdHTk5MmTFpeqdTodY8eOzTaRMRgMTJ482aL24/Xr1/n+++9Rq9VZzvx/VMZl6syGoGS8jvbu3Wux/caNG0yaNOmx537UgAEDUKlUzJo1y6IGb4bbt29ne/tSpUrRtGlTbt68aVUyavv27ZmOn83L+MHU+9SrVy/OnDnD1KlTM/07un37tkUildG+WX2xedz+vHrN3rhxI9MvsklJSSQnJ6NWq81DMbJqt1OnTmVaGSUrgwYNQqPRMGHChEyTS71ez549e8y/nz9/nnv37lkd9+DBA3Q6XY6GGwDm8md58bf8NHLz91m6dGliY2M5e/asxTl+/vlnduzYYXXujNrWW7du5ffff7faHxUVleX7fIUKFdi0aRMBAQEMHDjQaujU7t27LcaZZ8h4bjJ7Ho4cOYIkSRal54TCSQw5EGzi/fffR5Zlpk2bRtu2balZsyZ16tTBw8OD+Ph4wsPDzR/kjRs3Nt/Oz8+PHj16sGrVKpo1a0arVq1ISEhg165d2NvbU61atSyLlDdq1IiffvqJY8eO0bBhQyIjI1mzZg2yLDN37tynLtmV13ITb9++fTl16hSLFi2iZs2atG7dmtKlS5vbcv/+/bRq1cqqrNGTGDNmDMeOHWPr1q3Url2bdu3a4erqyu3bt9m9ezcjR45k2LBhKBQKBg8ezOzZs2ncuDEdOnRAp9MRFhZGbGwszZo1syp6n6FKlSocPXqUli1bEhoaSmxsLGvWrCEhIYEpU6Y8tlYuQKtWrTh+/DivvfYabdq0wd7enlKlStG7d2/atWvHc889x/z58zl37hzVq1cnIiKCLVu20KZNm1zNaK5YsSJffPEF7777Li1btjTXoY2NjeX06dOkp6dn+TgzzJo1i7Zt2zJmzBh27NhBtWrVuHHjBn/99RcdOnRg48aNFjPK8zL+DDNmzODatWtMnz6d5cuX07hxY/z8/Lh37x5XrlzhyJEjTJkyxbzSWfny5SlZsiQHDhwwF9jPGMNYtWpV6tevj5OTE6tXr0atVlOqVCkkSaJXr16ULl06z16zZ8+e5bXXXqNmzZqEhIQQEBBAXFwcW7ZsITY2lrffftu8CEHv3r356quvGDduHGFhYZQtW5arV6+yZcsWOnfunOnCAJkpX74833zzDcOHD6dRo0Y8//zzlC1bFoPBwO3btzl06BDp6emEh4cDsGvXLj766CPq169vXgzm3r17bNy4EaPRyLvvvpuj+23evDlubm7s2LHDpitX5ebvc+jQoezYsYP27dvTrVs3XF1dOXHiBAcPHqRr166sXbvW4twajYaffvqJF198kSFDhvDzzz9Tr149tFotV65c4e+//+by5ctZDu8JDg42L54wbNgw0tPTeeONNwB47bXXcHJyom7dupQuXRpJkjh27BgHDhygTJkydOvWzeJcGRUYmjVrluOhJYLtiIRWsJkPPviAF198kR9++IGwsDBWrFhBSkoKTk5OlClThjfeeIOXX37Zalb2vHnzCA4OZvXq1Xz//fd4e3vTvn17Pvzww2zH5AYHBzN79mwmT57M4sWL0Wq11K5dmzFjxmRZGsmWchvvjBkzaNOmDYsXL2bv3r3Exsbi5uZGYGAgAwcO5KWXXsqTuDQaDStXrmTJkiX8/vvvLF++HIPBgJ+fH61bt6ZVq1bmYzNKgv3yyy8sWbIEV1dXWrZsyYQJEzKdTZ/B3d2dP/74g8mTJ/PLL7+QlJRExYoVeeedd3JUGxZMRfsTEhLYtGkTc+fORa/X06RJE3r37o2Tk5N5AtHevXs5cOAAwcHBvP/++wwfPjzHiU2Gfv36UblyZebNm8fBgwfZtGkTnp6ehISEWJXiykzFihXZtm0bn3zyCXv27GHv3r1UqVKFX3/9lUuXLrFx40aLITZ5HT+YxvKuX7+eX375hRUrVrB+/XrS0tLw8fEhKCiISZMm0b17d/PxSqWSX3/9lcmTJ7NlyxZzlYXAwECqVq2Ku7s7v/76K9OnT2fNmjXmXvqGDRuae0rz4jVbq1YtRo8ezd69e9m1axexsbF4enpSoUIFPv/8c4skJSAggE2bNjF58mQOHjzIzp07KV++PF988QUtWrTIVbu99NJLVK1alfnz57N7927zl2p/f39eeOEFi2ENrVu3JiIiggMHDrB582YSEhLw9fWlfv36DBkyxOJvJjuOjo707duXb775hrNnz1K1atUcx5uXcvP3+fzzz/P7778za9Ys1qxZY17UYt26ddy4ccMqoQXTcxoWFsbcuXPZunUrR48exdHRkeDgYHPN5+xk1Afu1q0bo0aNIjU1laFDhzJ58mR27tzJmTNn2LFjByqVipIlSzJmzBgGDx5sNXFuzZo1pKWlMWDAgKdvNCHfSXFxcdb974JQjISFhdG5c2f69OmT5WSnwqSoxZuXbt68SY0aNWjSpAkbNmywdTiFwltvvcWKFSs4cuRIrlZzEoqnmzdvUr9+fV599dVcLYaQV/f9LP19Nm/enOTkZA4dOpSr8mqCbYgxtIIgCDYmyzJ379612r57925Wr15NxYoVRTIrABAUFMSwYcPMFRuE/LF27VpOnz7Np59+KpLZIkI8S4IgCDZmMBioUqUKzZs3p3z58qhUKi5cuMCuXbvQaDTMnDnT1iEKhcjo0aOxs7Pj1q1bORpPLuRexkpwhW2ysJA1kdAKgiDYmFKpZMCAAezZs4djx46RkpKCl5eXeQxgjRo1bB2iUIi4uLgwduxYW4dRrD3tqpFCwRNjaAVBEARBEIQiTYyhFQRBEARBEIo0kdAKgiAIgiAIRZpIaAVBEARBEIQiTSS0+SxjHWih4Im2tw3R7rYj2t52RNvbjmh72ylMbS8SWkEQBEEQBKFIEwmtIAiCIAiCUKSJhFYQBEEQBEEo0kRCKwiCIAiCIBRpYqUwQRAEQRCeiF6vJzk52aYx2NvbEx8fb9MYnlX50fZOTk6oVLlPT0VCKwiCIAhCrun1ehITE3F3d0eSJJvFYWdnh729vc3u/1mW120vyzJxcXG4uLjkOqkVQw4EQRAEQci15ORkmyezQvEiSRLu7u5P1OsvemgF4XH0ekAGpQrEG7cgCIKZSGaFvPakrymR0ApCFqTbN7BbvhDl2aNIBj0AslIFKhWo1MgqFSjVmf4uqx5ut/jd9E92dkV2dUd2cUd29TD97OqB7Oxmuo0gCIIgCLli00/Pffv2MW/ePE6dOsWdO3eYP38+ffv2zfTYUaNGsWTJEj799FNGjBhh3p6ens6ECRNYtWoVaWlpNG/enC+++IISJUoU1MMQipvUZDR//oR62yokg8Fil2TQg0EP6WnkR7+E7OTyMNn1sEx6XdzMya/R1QPZxR2cXUChzIcoBEEQBKFosWlCm5ycTOXKlenTpw9DhgzJ8ri1a9dy7NgxAgICrPaNGzeOjRs3snjxYjw8PBg/fjy9evVi9+7dKJXiw17IBVlGdWA7muULUcTF2CQEKTkRKTkR7tx67LGyQoHxuUro67VAX7c5srd/AUQoCIIgAISFhdG5c2euXr2Kl5eX1e8AGzZs4KOPPuLmzZu8/PLLLFiwIN/iWbp0KR988AG3b9/Ot/sozGya0LZp04Y2bdoAMGzYsEyPCQ8PZ+zYsfz555+89NJLFvvi4+P55ZdfmD9/Pq1atQLg22+/pVq1avz999+0bt06fx+AUGwobl3D7pe5KC+eynS/rFRa9dbammQ0orzyD8or/2C37BsMjya3voG2Dk8QBKFQi4qK4osvvmDLli1ERkbi5eVFlSpVGDRokDk3yY0GDRpw8eJFPD09zdtGjBjB66+/zqBBg3BycsrL8K28+OKLTxR3cVGoB+zp9XoGDhzIe++9R0hIiNX+kydPotPpCA0NNW8rWbIkISEhHDp0SCS0wuOlJKFZ8yPq7WuQjEar3UYvP9JfGY6hTjPTBoMe9DrQ65H0Ouvf9Xow6JD0/263+F2rRUqKR0qIRUqMQ0qIM/2cEIeUlIAkW8eQU8pr51FeO4/d8oUYgiqgr98Cfb0WyH4ln/icgiAIxdHNmzdp164dzs7OTJo0iapVq2I0Gtm9ezejR4/m7NmzuT6nRqPBz8/P/HtcXBwPHjwgNDSUwMAn72TQarVoNJrHHufg4ICDg8MT309RV6gT2qlTp+Lp6cmAAQMy3R8VFYVSqTR37Wfw8fEhKioqy/Nevnw5T+N8nIK+P+FfWba9bMTz9EECd65EnZxotduoVBHVsA13m3ZAVtvBlSs5vEcVSCpQY/qXG0YjqtRkVCkJqJITTf9STP+rM7alPLI9LSXLUylvXkJ58xJ2K74jxa8kcZXqElepDuleBTMsQbzmbac4t/27/9gBMLtKuo0jyVxxbvvM2NvbY2dnZ7Xdf1nBDtm628eLtLS0XN3m3XffRZZlNm/ebNFz+vrrr9O1a1fS0tJYuHAhy5cv58aNG7i5uREaGsqkSZNwc3MDTIkmQFpaGmlpaezbt48ePXrwzz//cOHCBXr06AFAly5dAFi1ahVNmjRhw4YNzJw5k2vXruHt7c3rr7/OyJEjzbP769atS69evbh9+zYbN26kefPmPP/883z44Yf89NNPTJgwgfDwcGrVqsXs2bMJCgoC4Pfff+fDDz/k2rVrANy4cYNJkyZx/PhxkpKSKFeuHO+//36e9+Lmtu1zIiEhIdM8rnz58lneptAmtGFhYfz222+EhYXl+bmza5C8dvny5QK9P+FfWbW94uZl7H6eh/JK5t/A9dUbkN53BM7+JSmX30E+Af3Df1LMPVRH9qA6sjvLxwLgeC8Cx3sRBP79J4aSZdDXbYG+fkvkEsH5Ep94zdtOcW/7vXtNYwML42Ms7m2fmfj4+EKzoEFu4oiNjWXXrl1MmDDBqkPs0XNpNBqmTZtGcHAwt27d4oMPPuCjjz5i0aJF5v0Zx9vb21v83qxZMw4ePEjDhg35+eefadCgAR4eHpw7d4633nqL9957j5dffpnjx4/z7rvv4uHhweDBgwFT2apFixbx3nvv8cEHHyDLMgcPHkSr1fL111/zzTffYGdnx9ChQxk3bhyrV68GQK1WI0mSOX6dTkfbtm2ZOHEiDg4OrF69mgEDBrBv3z4qVKjwhC1tKS0tLV9eA66urpQqVSpXtym0Ce3evXu5e/euxVADg8HApEmTWLBgAefOncPX1xeDwUBMTAze3t7m46Kjo2nUqJEtwhYKs+RENKsWo975V6aX9o3e/qT3HYGhVuMiUW9W9vJD164nunY9kR5EozpqSm4Vl88gyXKmt1FGXEcZcR27P5dgDAwyjbmt1xJjyTJF4jELz7ZlrT0ff5AgPMa1a9eQZfmxSd2jc3uCgoL45JNPeOWVV1i4cCEKRfbrUmk0Gnx8fADw8PAwD0WYP38+TZo04cMPPwSgXLlyXL16lblz55oTWoDGjRszcuRI8+8HDx5Er9cza9Ys8xenESNG8PbbbyPLcqa1W6tVq0a1atXMv7/33nts3ryZtWvX8v7772cbf1FUaBPagQMH0rVrV4ttPXr0oEePHvTr1w+AmjVrolar2bVrFz179gTg9u3bXLx4kQYNGhR4zEIhZTSi2rsZuz++RUq0XnNaVqvRdXwFbcdXQGN9+awokD190LXpga5ND6S4GJTHwlAd/hvlxdNZjstVRN5Es/ZnNGt/xuhXEqOnT57EUi4lBXtnZ1AoTGXFlEpQKJEVyn+3KRSPbP/353+PV5iOV6sxlnwOQ7kq4OSSJ/EJRVf70s/u+EAh78hZfOH/r927dzN79mwuXbpEQkICBoMBrVbLvXv3Mq26lBMXL160uuTfqFEjpk+fTkJCAq6urgDUqlXL6rZ2dnYWVwH8/f3RarXExcXh4eFhdXxycjLTp09ny5Yt3L17F71eT1paGlWqVHmi2As7mya0SUlJ5rEeRqORiIgITp8+jYeHB6VKlTJ/u8mgUqnw8/MzP6Fubm689tprTJo0CR8fH3PZripVqtCyZcuCfjhCIaS4fhG7X+agvHo+0/36mo1J7/t2saoKILt7oW/dDX3rbkjxD1Ae32tKbi+czHTiG4DiXgSKexF5cv/5kXbKkoSxRDDG8tUwVKiGoXxVU5ky0assCIVOXP+CrQOf2zGcZcuWRZIkLl26lOUx4eHh9OrVi9dff50PP/wQT09PTp06xYABA8xjZ/Pao72smVVEUP1n4Z2M441ZvK9/9NFHbN++nU8//ZSyZcvi6OjIkCFD8i1+W7NpQnvixAk6d+5s/n3q1KlMnTqVPn365LhW29SpU1EqlfTv39+8sMLChQtFDdpnXVI8pTb+gsOJsEwvvxt9A03DC2oW76Epspsn+lZd0LfqAglxqI7vNY25PXcsy+S2MJJk2TxcQr3rLwCMHt4YKlQzJ7nGUs+JhSaKuSUXTeu7vxGSv+WPhOLNw8OD1q1b89133zF48GCcnZ0t9sfFxXHixAm0Wq05xwDYvHnzU993RhWmRx04cIASJUrg4pK33QEHDx6kd+/e5qvdaWlpXL9+nbJly+bp/RQWNk1omzVrRlxcXI6PP3PmjNU2Ozs7Zs6cycyZM/MwMqFIkmWkO+Goju5Bs3kFzskJ1odo7NB26ouufa8iO7zgibm6o2/ZCX3LTpAUj+r4flRHd1ss7VuUKGLvozi0Cw7tAkC2d8RQrsrDJLcqhrKVwE5coi5ORu2PA0RCKzy9WbNm0bZtW1q1amW+sivLMmFhYcyePZvff/8do9HIN998Q+fOnTl69CgLFy586vsdPnw4oaGhTJ06lZ49e3L8+HHmz5/PRx99lAePylLZsmVZv349HTp0QK1WM336dNLTC2eFkLxQaMfQCkKOGI0orp1HdWwvquN7UdzNeoUtfZ1mpL8yXKyoBeDshr55e/TN20NyIopb15CMebNwREREBCUDA8FgAOO//ySDAYzGh78bzfulR37GaHj4sxHJaEBKjENx+R8Ut69nOdEtg5SWgursEVRnjwAPV1ILqoChfFVzkiu7W89oFoqOfhUcbR2CUEwEBweze/duvvjiCyZNmsSdO3fw9PSkatWqzJkzh6pVqzJt2jTmzp3LlClTqF+/Pp9++in9+/d/qvutWbMmS5YsYdq0aXz55Zf4+voyatQoBg0alEeP7F9TpkxhxIgRdOjQAXd3d4YOHVqsE1opLi4uZ6OjhSfyLJZyyXc6LcpzJ1Ad34vyxF4U8bHZHm70L0X6qyMwVKtfQAE+2/LlNZ+ciPLKOZSXz6C8dAbFtfNIutyPA9NXq0/6G6OL7Zca8X5jO89i28fHx5trstpSfpWOEh4vv9r+SV5boodWKBqSE1GdPoTy+D5Upw8ipaU+9iYGtQZ9t37o2vYE9eNXWREKMScXDDUaYKjxsHqJXofixiWUl86YktzLZzOtYPFfqjOHUU4YQPprI9E3fkFMKhMEQSgmREIrFFrSgyiUJ/ajOrYX5YUTpkvWjyErVRgq10ZfuwmXPUtRpmbtAohUKHAqNcZyVTCWq4KO3qbx03dvmRLcS6YEN6uqDVJqMvaLPkd/Yh9pb4wGZ9v3MAk5cyfF9B4Q4Cgm/wmCYEkktELhIctIkTdNM/GP7UV5/ULObmbviL5GQwx1mqKvVh8cTTNW9c/YMpTPNElCDiiNPqA0+hYdTZviH6C4fBbl5bMo/zmG8tZVi5uojuzG8fJZ0gd8gKG6qFtdFFRafhco+LJQgiAUfiKhFWxOin+Aatc61Pu35bgWqtHdC0OtJujrNMVQsaYYUiBYkd08MdRtjqFuczAaUe/4E83yhRZjbxVxMTh8MQZt625oew0BOzEOrzDzd8h+dSZBEJ5dIqEVbEZx/SLqratQHd6FpNc99nhjQGn0tZuir9MUY5mKptWmBCEnFAp0L7yIvkod7BdOQXnTsqC6ZsefqP45RtqgDzGWrWSjIIXHudD7yVZnEgSh+BMJrVCw9HpUx8JQb12F8srZxx5uKFfFlMTWboIcULoAAhSKMzkwiNSJ89Gs/Rn1uqUWywIr7t7C4bPhaLv2Q9e5LyjF26MgCEJRId6xhYKRGIf67/Wod/yJIvZ+lofJKvXDSV1NMdRqLOqGCnlPpUbbYwD6Gg2x/3YKiqhI8y7JaMRuzY+oTh0kbfCHyP6lbBioIAiCkFMioRXylSL8Kuptq1Ad2Iaky3pYgdGvBLrnX0TXtK15Upcg5CdjuSqkfPo9dssWoP57ncU+5bXzOH70Ful9hpqWDRblvQqFFn9FAbC7i6+NIxEEobARCa2Q94wGlMf3o9m2EuWFU9keqq9SF12bHqZZ5mJMrFDQ7B1J7/8/9LUaYbd4JoqEfxfpkLRp2P80G/2J/aQP+EBcLSgETsU8fqy9IAjPJpHQCnknORH17g2od6xBcf9elofJGnv0TdqgfeFF5BLBBRefIGTBULMxKVN+xP7HmaiO77PYpzp9COX4/qT1f89UMUGwmb87+9g6BEEgLCyMzp07c/XqVby8xBfdwkJ0iQlPTYq8id2SL3Ea1RO75QuzTGaN3n6k9xpC8pwVpuVHRTIrFCau7qS98xlpAz5Atnew2CUlJeAwbyJ2302F1GQbBSjU9NZQ01uU6BOeXmRkJCNHjqRy5cr4+PhQqVIl3nnnHW7fvm1xXMeOHXn//fdtFKWJwWBgzpw51K9fn4CAAIKCgmjZsiULFy40HzN06FB69eqV63NPnTqVRo0a5WW4NiN6aIUnk5aC6tRBVLs3ovrnaLaH6ivWNA0rqNUYFGKFH6EQkyT0zTtgqFQL+0Wfo7x0xmK3eu8WlBdOkvbWhxgr1rBRkIIgPI0bN27Qtm1bgoKCWLBgAc899xzXr1/ns88+IzQ0lK1btxIUFFTgcWm1WjQa6y9s06ZNY/HixcycOZM6deqQnJzM6dOnuXXrVoHHWJiJhFbIueREVCcPoDq6G+WZIxYF6v9LVmvQN3oe3QsvYixdrgCDFISnJ/sEkDpuDupNy9Gs+gHJoDfvU9y/h8O0Ueg69EH70gDxJa0ATT2RAMC4Wq42jkTIjnO/lgV6f2nfbs7V8e+//z4KhYI///wTR0dHAEqVKsWff/5JnTp1eP/99/njjz8YOnQo+/btY9++fXz33XcAnDr177yQs2fP8sknn3Du3DlCQkKYM2cONWvWNO8/dOgQH3/8MSdOnMDd3Z327dszefJkXF1Nr9+OHTsSEhKCo6Mjy5Yto3Tp0uzatcsq3k2bNvHmm2/So0cP87YqVaqYf546dSrLli0DwN3dHYB169bRrFkzJk+ezPr164mIiMDHx4fu3bvz4YcfYm9vz9KlS5k+fbrF7ebPn0/fvn2Jj49n4sSJbNiwgbS0NKpXr86UKVOoVasWAPHx8bz//vvs2LGDpKQk/P39GTx4MMOGDcvVc5GXREIrZC8xDtXxfaiO7kH5zzGLD/bMGD280bXujq5lR3BxL5gYBSE/KJToOr6CoWo97L6dgvL2DfMuSZbRbPgNxd1bpA0eL1YYKyDTTyYCIqEVnlxsbCzbt29nwoQJ5mQ2g6OjIwMGDGDKlCnExcUxbdo0rl69Svny5Zk4cSIA3t7ehIeHA/Dxxx8zefJk/P39GTt2LIMGDeLQoUNIksQ///zDiy++yNixY5k3bx6xsbGMGzeOt99+m59//tl8n3/88Qf9+vVj06ZNyLKcacx+fn7s3buXqKgofH2tK3yMGDGCS5cuERsby7fffguAh4eH+TF9/fXXBAQEcPHiRUaPHo1Go2HChAm8+OKLnD9/ni1btrB+/XoAXF1dkWWZXr164erqyvLly/Hw8OC3336jS5cuHDlyBH9/fz777DPOnTvHL7/8QsmSJbl58yYxMTFP+ew8HZHQClakuBiUx/aaemIvnEQyGh97G0P5quhe6IG+TjNQiZeVUHwYg8qTOvlbNKsWo9n8h8U+1bEwHKa/S+qoqeDqbpsAnyFjarrYOgShiLt69SqyLFOhQoVM94eEhCDLMlevXqVOnTqo1WocHR3x8/OzOnb8+PE0b26aKPrBBx/Qrl07IiMjKVGiBF999RXdu3dnxIgR5uO/+OILmjdvTnR0ND4+pgmOpUuXZsqUKdnGPGXKFPr160dISAghISHUq1ePF154gc6dOyNJEs7Oztjb22NnZ2cV5wcffGD+OSgoiNGjRzNv3jwmTJiAg4MDTk5OqFQqi9vt3r2bM2fOcOXKFRwcTPMJJkyYwObNm1m+fDkjR47k1q1b1KhRg9q1a2Nvb0/p0rZf+EhkHgIAUkwUqmN7UB3Zg+LyGaQsvik+yujtj75eC/QNW2MMzvzNQRCKBY0d2j7DMNRoiN23n6OI+3dxEOXV8zh+OozU/81A9i9pwyCLP9EzKxQmj1729/f3ByA6OpoSJUpw6tQprl27xpo1a8zHZPTAXr9+3ZzQPjpEISsVK1bkwIEDnDx5kgMHDrB//3769+9PaGgoy5cvR5FNycu1a9eyYMECrl27RnJyMgaDAYPBkO39nTp1ipSUFMqVsxwumJaWxvXr1wEYMGAA/fr148SJE4SGhtKuXTuaNm362MeSn0RC+wyToiJRHd2D6shulNfO5+g2Rv9SpiS2bnOMQeVFwXnhmWKoXJvUSd9g/8VYlBHXzNsVUZGmpHbU5xjLV7VhhIJge0k//V2wd5iWluNDn3vuOSRJ4uLFi3Tu3Nlq/8WLF5Ekieeee+6x51Kr1eafpYefhRlJq9Fo5PXXX890TGlAQID5ZycnpxzFrVAoqF27NrVr12b48OEsX76cwYMHs2/fPpo1a5bpbY4cOcKbb77JmDFj+Pzzz3Fzc2Pjxo189NFH2d6X0WjE19eXTZs2We1zcTFdJXnhhRc4c+YMGzduZP/+/fTq1YuuXbvyzTff5Ojx5AeR0D5jpLgYVHs2mpLY8Cs5uo2hZBn0dVtgqNccY4kyIokVnmmypy+p47/C/utJqP45Zt4uJSXgMH00aYPHY6jXwoYRFl8n75smoorSXcKT8vT0pHXr1ixevJhhw4ZZjKNNSUnh+++/54UXXjCPQdVoNI/t0cxMjRo1OH/+fI4S4ycREhICQHKyqYxgZnEePHiQgIAAi2EH/62MkNntatSoQVRUFAqFguDg4Cxj8PLyomfPnrz22mu88MILDBgwgNmzZ2NnZ/c0D+2JiTq0zxDVoZ04jn0du1WLH5vMGoIqkP7SWyRP/4XUKT+i6/4GxpLPiWRWEAAcnUkbPc20VPMjJJ0W+/mTUW9ZYaPAireW66JpuS7a1mEIRdzMmTPR6/V069aN3bt3ExERQVhYGN27d0eWZWbMmGE+tnTp0hw7dsw86cmYgzklACNHjuT48eO8++675uEHmzdvZtSoUbmO9/XXX2f+/PkcPXqU8PBwwsLCeP/99/H19aVBgwbmOM+fP8/ly5eJiYlBp9NRrlw57ty5wx9//MGNGzdYvHgxq1atsjh36dKluXXrFidPniQmJob09HRatmxJw4YNeeWVV9i2bRs3btzg8OHDfP755+zfvx8wjetdv349165d4+LFi6xbt47g4GCbJbMgEtpnQ3oadj/MxP6bT5CyKQpvKFuZ9N5DSZ61jNRPFqHr3BfZv1QBBioIRYhKTfrAsWi79bPYLMkydr/NR7N0Hhhz37MjZK2Gl5oaXurHHygI2ShTpgy7du2iYsWKDBkyhJo1a/LWW29RoUIFdu7cadErOWLECDQaDQ0bNqRs2bI5rv1atWpVNm7cSHh4OJ06daJp06Z88skn5rGzudG6dWu2bt1Knz59qFu3LkOGDKFUqVKsXbvW3JPcr18/KlSoQKtWrShbtiwHDx6kffv2vPPOO4wbN44mTZqwa9cuPvzwQ4tzd+nShRdeeIGuXbtStmxZVq5ciSRJ/PHHHzRr1oyRI0dSr149+vfvz5UrV8zDJezs7Pjss89o3bo1bdu2JSkpid9//z3Xjy0vSXFxcY+f/SM8scuXL1O+fHmb3b/i1jXsvvkEZeQNq32yJGGsUB193ebo6zZD9rQuB1KUPU3by7JMVKqR8CQD99MMlHZWUdFdhVIheqgfx9aveVtQ7dmE3ZJZSP+5dKev06xAy3o9i21fWDyLbR8fH4+bm5utwyAtLQ17e1E6zxbyq+2f5LUlxtAWV7KM6u912C392moBBFmlRtv9DfRN2yG7P5vrUBtlmXupRsIT9dxKNhCeZCA8UU94koFbyQZuJelJ+0/nmotaora3hno+Gur5aqjro8bLXhTVF0DfvD2yhzf2X09CSksxbzeV9RpN6qjPRVkvQRCEfCQS2uIoORH7H2aiOrrHapfRvxRpwyaaKhQUYwajzL10iZh76aZkNclAeJKeW4/8r83ZUCizRJ3M7jvp7L6Tbt5W1lVJXR8N9X1NiW5lDzUq0Yv7TDJUq2eaLPbF2P+U9TonynoJgiDkM5HQFjOKK/9gv+ATFPfvWe3TNW1L+msjwd4xk1sWDrIsk2qQSdTKJOqMJOpkErQyCTojiVrT74k6+ZGfjSQ8/D3hke0JWiN62QG4/9j7fBpXEwxcTUhl+dVUAJxUErW81eZe3Ho+GnwcRC/us8JYuhypE7/B/sssynq9OxVjuSrZnEHITsXf7wBwoXfAY44UBOFZIxLa4sJoRL1xGZpVi61W9pLt7EnvNxp9kzY2Ci5rcelGNt9KY/3NVA5GaYlNN2IoJKO6XdUSpV1UeGgk/onV8yD98V26yXqZvXe17L377zCPYBelKcH10VDXR4O/oxJHlYSjSkKt+Ld+oVA8yF4Py3rNm4jq3HHzdikpAYdp75I2ZAKGus1tGGHRdTc1l5dVBEF4ZoiEthiQ4mKwW/S5RU3MDIag8qQNm1ioqhXcTTGwITyV9TfTCLuTjt5GCaybRqK0s4rSzsqH/1SUeuRnd7t/i4DIssy1BANHorWmf1Fa/onV5Sj5vpFo4EZiKiuupVrtU0qmXl2Hhwnuv/8UOKgknB7+nvGzwyP73TQSTf3t8HMUPcCFjqMzaf+bjt2Ps1Dv3WLeLOm02H89CW2fYeja9rRhgEXT+V7+tg5BEIRCSiS0RZzyzGHsFk1FkRBrtU/b5iW0Lw8Cte2LkF+N17M+PJX1N1M5Eq0rkPt0U8kEu2ko7ax8mKiqLBJXN03Oq9ZJkkRZNxVl3VT0LmcaspGsM3IiRseRqH+T3Oi03PUgGWRI0Mkk6J4sq9co4O2qzrxb3QUXtajCV6g8LOsle/mjWfuTeXNGWS/p/l20fYaBQnwhyakA8eVNEIQsiIS2qNLr0KxajGajdd032dmVtLfGYqjZ2AaBPYxBljn9QMf6m2lsuJnKuTh9jm9rpwQXtQIXtWT6XyPh+uj/agkXzSP7H/7uqpZwfbjdWa0g4voVypfPv0k4TmoFTf3taOpvKiQtyzI3kwwcidJyOFrL0WgtZ2J0+doDrTXCl6eT+O1yCpPqutGrrAMKMYSh8JAktC/2x+jth92PsyyGA2m2rkLxINpU1ktju2LkgiAIxYFIaIsgKSoS+wWforx23mqfoWIN0gZPQPbMffHmp2UwyhyK0j7siU0jPClnReVD3FR0CrKnU5ADlT3U2CmLZkImSRLBLiqCXVT0LGvqxU3RGzn1SC/u+Vg9STojKQaZFJ2cZ8nu3VQjQ8Ni+f58EtMbulPXx/a98sK/9M07PFLW69+hJ6qje3CIvU/aqCnIrh42jLBoGLnPdCVqbhPRVoIgWBIJbRGjOrQTux+/sFrxS5YUaLv1Q9fl1QK9hJlukNkdmc768FQ2hafl+JJ7bW81nYMc6BRkT3m34rvyj6NKQSM/Oxr5Zd4DpzXIpOhN/1L1Msl64yM/m/5PsfjZaD5+a0Qad1Is2/vYfR3Pr4+mV1kHJtd1E5doCxFDtfqkfvgV9l+Osy7rNeoljGUqYgipgaFSTQzlqoJD4a1GYis/XTLV+BUJrSAI/yUS2qIiPQ27pfNQ795gtcvo6UPa4AkYK9bIl7tO1hlNiw8kPlrL1fTzxTg9STnoZlRK0MTfjk6l7ekY5EAJJ5FoAWiUEhqlhPsTXHFO0hmZczqJef8kkv6fzvDlV0295P+r4cKwys7Yq4pmr3dxYwwq/7Cs1xiUEdfN2yWDAeWVf1Be+Qc2/IasUGAMDsFQsYYpya1QDRydbRh54TCnsbutQxAEmxo6dCgPHjxg+fLlWR5z8+ZNatSowa5du6hVq1YBRmdbYunbfJYXyyEqwq9gt+CzTJev1ddqQtrAD8D5yZcfTNQZLRYcyEhWTatnGYjJQbmqzNgroVWgPZ2C7Glfyh7PAl5V61lZivJGop6PjsSz7mZapvuDnJV8Vt+NTqXtC6RE2LPS7k8lJcmqrFd2ZEmBsXQ5U4JbsQaGCtXB2dXqONH2tvMstn1RXfp26NChLFu2DACVSkWJEiXo3Lkz48aNw8nJKb/CzBPx8fHIsoy7uzsAHTt2pHLlysycOdN8jMFg4P79+3h5eaFS5W+/5fLly5k3bx5Xr17Fy8uLQYMG8c4771gc89133/Hdd98RHh5OyZIl+d///kefPn2yPa9Y+raYke7dRvPnElQHtiPJlt87ZJUabe+h6J7vDjlMUnRGmT130vk7Mp0bGcu8JhlyVF81p1zVEm1LmcbDti5hh7OYeZ/vgl1U/BLqxe7IdMYdirOagHczycBrOx/QIsCOqQ3cqOxRfId4FBkPy3qpNyxDvW8Linu3sz1cko0ob15CefMSbFmBLEkYSz2HIaTmw17c6uDiXjCxC0Ix0LJlS7799lt0Oh0HDhzgnXfeISUlhS+//NLqWL1ej1KpLBQ1w3OS5CmVSvz8/PI9lm3btjFs2DCmT5/O888/z8WLFxk5ciT29vYMGjQIgMWLFzN58mTmzp1L3bp1OXbsGCNHjsTd3Z327dvnaTxZ9tDu27fviU7YpEmTpwqouDlx/jK1KuXuW7v0IArNX7+g2rMRyWA9sSo3y9caZZkD97Ssvp7Kn9dTn7i3NTu+Dgo6ljYlsc387dAUkkldz2KPid4os+RiMlNOJBCbbv2nrZBgQIgTH9Z2xcMuf75sPIvt/rSkB9EoL55GeeEkyosnUdy5letzGEoE88A/GOcW7TFUqQP53DNjC5vCTRPq2pd2sHEk1p7F131R7qH972X7d955hy1btnDx4kWmTp3KX3/9xdtvv83MmTMJDw8nPDyc2NhYxo4dy+7duwFTUjx9+nRKlCgBYL7d0KFDmTFjBvfv3yc0NJR58+bh5eUFgNFoZNasWfz0009ER0dTrlw5xo8fT8eOHc2xTJ8+nV9++YWoqCjc3d1p1aoV3377rVXsj/Y0Zzh16hSA1ZCDffv2MXHiRM6ePYurqysvvfQSH3/8MRqNaQJxx44dqVixIm5ubixZsgSFQkHv3r355JNPUCgy/6wYOHAgycnJFjF8++23fPXVV5w9exZJkmjTpg116tRh6tSp5mPGjx/PsWPH2Lx5c5bPUZ720Hbq1Mni24gsyzn6dvLgwYNcBVCcpRtkOhx2oOylKFoH2hFawo6GfnZZz+JPiEOzfinqnX8i6TKv1ZqT5WtlWebEfR0rr6fw5/VUIlOeLolVSVDC6eGCAy4qSv3n51LOSlEqqpBQKSQGVnKmx3OOfH4igR8uJFss/mCU4bsLyay8nsKHtVzpH+KESiGeO1uTPX3QN2qNvlFrwLRYivLiaRQXT5mS3Ns3HnsO5e0b+Ny+Acf+RnZxQ1+3BbpGz2MsXxWy+EAqavrsMH2+xPUvYeNIhOy4/2i64vDo89RrewxbbqWxrLWn+QvJkovJjNofR78KjuaJfndSDFRafhd/B4XFEsct/oriVIyOvzv7UNPblIRNPZHA9JOJ3O3j9dQx29vbo3vkc/fmzZusXLmSJUuWoNFo0Gg0vPLKKzg4OLBu3ToA3n//ffr27cuuXbvM+VF4eDjLly9n6dKlpKamMmrUKIYPH87vv5tKbC5YsIB58+bx5ZdfUqtWLZYvX85rr73G33//TfXq1Vm7di1ff/0133//PZUrV+b+/fscOXIk05inTZvG1atXKV++PBMnTgTA29ubiIgIi+MiIyPp2bMnvXr14ptvvuH69eu88847KBQKpkyZYj5uxYoVDB48mK1bt3LmzBkGDhxIzZo1eemllzK9//T0dOzsLCeAODg4cPv2bcLDwwkKCiI9Pd3qy4aDgwPHjh1Dp9OhVufdFcMsE9qMJyyDVqtl4sSJaLVaXnvtNcqVKwfAlStX+OWXX7Czs+OTTz7Js8CKg4P3tKQaJc4+0HH2gY65Z5NwUEo09dcQWsKe0BJ2VHBTIaUkodn8B+qtKy1K+jzKUCIY7UtvYaiddQ/4uVgdq66lsOp6KjcSc1YyC0CtgJJOSkr9Z9WsjAUJAhyVIukpYjzsFMxs6E7/ECfGHYpn9510i/2x6TLvH4znhwvJTGvgRovAnPduCPlPdvdC36AVNGhl2pAQh/LSaZQXTAmuIuKa1TCkR0mJ8ah3/YV6118YPX3QNwhF37C16apOEf7y2baUeJ0Kee/YsWOsXLmSFi1amLdptVq+/fZbfH19Adi1axf//PMPJ06cICgoCIDvv/+eWrVqsXv3blq2bAlAamoqCxcupFQp0+qcs2fPpn379ly9epWyZcvy9ddf8/bbb9Ozp2mlwPHjx7N//36+/vprFi1axK1bt/Dz8yM0NBS1Wk2pUqWynNjl5uaGWq3G0dEx2yEGixcvxt/fny+++AKFQkFISAiTJk3i3XffZfz48Tg6mjrIQkJCGD9+PADlypXjp59+Yvfu3VkmtK1bt2bcuHHs3LmTli1bcu3aNb7++msA7t27R1BQEK1bt+aXX36hc+fO1KpVi5MnT/Lzzz+j0+mIiYnB3z/vVv/LMqFt2rSpxe8ffvgh9vb27NixwyojHzhwIJ06dWL79u20atUqz4Ir6nbetp6kk2qQ2XY7nW2303E0pDE+ajvDb6xDk56U6TmMvoFou/dH3zA003Jc1xL0rLqWwurrqZzPweIFbhqJzkEONPW3I8jFlLj6OyhQioS1WKrsoebPtl5sCE9jwpF4qy865+P0dN0SQ6fS9rxXw4VqnmrxWiiMXN0x1G2OoW5z0+9JCY8kuKdQhF9BkjO/EqN4EI1m03I0m5Zj9C+FvmEouoatkQNKF+ADyBvLn3/6njgh/2XWg57Zc/dGiBNvhFhOwgpwVGZ6+91dfK22javlyrharqSlZT4hNjvbt2+nRIkS6PV6dDodHTp0YMaMGeb9gYGB5mQW4OLFiwQEBJiTWYDg4GACAgK4cOGCOaENDAw0J7MAdevWRaFQcPHiRXx8fLhz5w4NGza0iKVRo0Zs3boVgG7durFw4UJq1KhBaGgozz//PO3bt7fKu3Lj4sWL5jgevU+tVsu1a9eoWrUqAFWqVLG4nb+/P9HR0Vmet1+/fly+fJlXXnkFnU6Hi4sLQ4YMYdq0aeb7ev/997l37x5t2rRBlmV8fX3p06cPc+fOzXIow5PK8SCrFStW8N5772XaqA4ODrz88svMmjXLovv6WXcv1YCEjIxlgqAx6ngrcifjbq7FXxef6W3T3Lwxdu+HsVl7q7FwEUl61txIZfX1VE7cf/wysk4qiQ6l7XmxjAOhJeyL7MIFwpORJIlOQQ48X8KeBeeSmHUqkeT/lFpbH57G+vA0XNUS9Xw1NPKzo6GfhtreahxVxeNydbHi7IqhdlMMtR92PKQkobx0muRdm/C6dAIpJfMvyIq7t9D8+ROaP3/CEFQefcPW6BuEIntZJwuCUJw1btyYuXPnolKpCAgIsLr0nZtqB3kxWSzjHCVLluTo0aPs3r2bv//+mwkTJjB9+nS2b9+eLxUYHo39v20gSRJydleCJImPPvqITz75hHv37uHt7W0eXxwcHAyY8sP58+czZ84coqKi8Pf3Z8mSJbi4uODt7Z2njyXHCW1KSgr37t3Lcv+dO3dITc38cvmzamFzTwZ4xxDhUIIdt9PZfSuZ1td389GN1QSlx2R6myi1K9NKd+HbwNbYRdrRYk88oYH21PfVcOBeOquup3Lgnvax922nhBdK2NPjOQfalLTHSVQbeObZqyTere5C73KOfHw0nt+vWv+9JuhkdtxOZ8dt0xAFtQJqeqlp6GdHQ18NDf00eBVw+TUhBxydMdRszC0nH+yDP0J55giqgztQndiPpM2890p58zLKm5exW74QQ4Vq6Bq2Rl+vJbi6F2jogmALjo6OPPfcczk+PiQkhDt37nDz5k1zL+2NGze4c+cOFStWNB8XGRlJREQEJUuall0/duwYRqORkJAQXF1dCQgI4ODBgxbDGw4cOEBISIj5d3t7e9q2bUvbtm159913qVChAocOHSI0NNQqLo1GgyGTyeP/jX3NmjUYjUZzr+iBAwfQaDSUKVMmx22QFaVSSWBgIAArV66kfv36VsmqWq02T55btWoVbdu2tV0PbYsWLczd4F27drXYt3btWr799ttMG/tZ566GekH29Iw6iObkjyjuZj57OU7pyBelO/JViXYkq0zjxNK1MutupmVZX/S/lBK0CrTjxTIOdAxywE0jkljBWoCjkoXNPRlYScuYg3Ecy6aXX2eEI9E6jkTrmPdwWwU3FQ39NDR82JMb7FI4ytkID6k1GGo3wVC7CenpqaiO70d1cAfKM4eRDJkPS1JeOoPy0hnkX7/CUKWOqee2TjNwKFw1OTObbCQIBaFly5ZUqVKFQYMGMW3aNAA++OADatSoQfPmzc3HOTg4MHToUKZMmUJaWhqjR4+mbdu2lC1bFoARI0YwdepUypYtS82aNVm+fDkHDhww92wuXboUg8FAnTp1cHJyYs2aNajV6iyT79KlS3Ps2DFu3ryJs7MzHh7Wq+gNGDCABQsW8L///Y8hQ4Zw48YNPv74Y9566y3z+NknERMTw4oVK2jVqhXp6eksXbqUtWvXsmHDvwtAXblyhaNHj1KvXj3i4uKYP38+58+fZ8GCBU98v1nJcUI7a9YsunTpQv/+/fH19TVn9devXycqKooyZcpYjD/JiX379jFv3jxOnTrFnTt3mD9/Pn379gVAp9Px2WefsW3bNm7cuIGLiwvNmjVj0qRJFuNT0tPTmTBhAqtWrSItLY3mzZvzxRdfmL8J2JQs43r5FA4/T0MZfjXTQ/RqO3ZV78xk//YcSs79hAcJaOyv4aUyjnQJthe9Z0KO1fXRsK2TD2uup7LyWiqHorQ5qkl8KV7PpXg9Pz9chtTPQfEwwbWjkZ8GO7FUS+Fh5/Bv9YSkBFRH96A6tBPl+ROZTiqTjEZUZ46gOnMEeckXpkoJoV1NlRLElxbhGSZJEr/99htjxoyhc+fOgKmjb8aMGRZf6EuXLk2PHj3o06cPMTExtGrVinnz5pn3DxkyhKSkJCZNmkRUVBTly5fn559/plq1aoBpotfcuXOZMGECer2ekJAQfvnlF/Ml/P8aMWIEQ4cOpWHDhqSmpprLdj0qMDCQFStWMHHiRJo1a4abmxsvvfSSuTLC01ixYgWffvopsixTr1491q9fT506dcz7DQYD8+fP58qVK6jVapo2bcrWrVstxiLnlVytFJaWlsaPP/7I9u3buXXL1NNYqlQpXnjhBfr164eDQ+5qA27dupWDBw9So0YNhgwZwqxZs8wJbXx8PP369eP111+nWrVqJCQkMGHCBB48eMC+ffvMq1+MHj2ajRs3smDBAjw8PBg/fjzx8fHs3r0bpdKGyV1SPA5fjkN59Vymu2WVGl2rLug690V28wRM5Up23U5jZ2Q6u26nZ1sztq6PmhfLONIt2IFAsYxspp7FupBPwyjLXI7Xc/CelgP30jkYpc1VtYwMjkqZur721PfRUNdXTT0fMUyhoOT0NS/FxaA6vMvUc3v1/GOPN5Qqi651V/SNns+2ZOCz7Fl8vymqdWjzS0Yd2gMHDtg6lAKTX23/JK+tQrP0bYkSJZgxY4Y5oc3MhQsXaNiwIfv27aNKlSrEx8dTrlw55s+fz8svvwxAREQE1apVY+XKlbRu3bqgwrcmyzh8PBTl9QuWmxUK9E3boe3WD9kr6zIbRlnmdIyOnZHp7LidxqU4PQGOSrqVceDFMg4EuxS/oul57Vn8gMlrd1IMHHokwT3zQIfxCd4xnnNRUs9XQz0fDfV8NVTxUItScPngSV7zUlQkqkM7TcltxPVsj5UdnNA1bYsutCtyYN73sBRlz+L7jUhoLYmENu8U+6VvExMTAcxrGJ88eRKdTmcxdrdkyZKEhIRw6NAh2ya0koS2xwAcZr1v3qRr2Bpt9zeQ/Utlc0MThSRR01tDTW8No6u75GekgpCljC9R3cqYrr4k6owcjdJyIErLwXtajkZrSdE/PsO9lmjgWmIqyx9ORHNUSdT0UlPfV0NdHw31fTX4OoheXFuQfQPRdX4VXedXUURcQ3XQlNwqou9YHSulJqPZthrNttXoK9VC17orhlpNi+WqZIIgFC256qHdsWMHv/zyCzdu3CAuLs6qnIMkSZw8efKJAnlcD61Wq6Vz5854eHiYV9xYsWIFQ4YM4f79+xZjWDp37kzZsmWZM2dOpue6fPnyE8WYa7JMuV9mYbB34E6LbqT5lSyY+xWEAqI3wsVkBacSFJxMUHAqQckD3ZP1vAbaGanmaqSqi5FqLkYqOBkRxTlsRDbicv0C3kd34Xb5VLaLOGhd3Imp1Yz7tZqjd3HP17De/cdUNnJ2lfTHHCkUBHt7e3x8fGwdhlAMRUdHZ1pfOLurIDn+Wv3VV18xefJkfH19qV27NpUrV36yKJ+AXq9n0KBBxMfHW61b/CQK8rLQlT4jKVe5Co/vkxXy2rN4CdAWKgHdHv4syzK7zlwl2imQo1FaDkdrOftAZ7H8blYi0xVERivY8rCOt70Sanpp6FjantcqOOFuJ7Lbx8nT13yFEGjblZSYe6h3rUO1ewOKhFirwzSJcQTsWYf/3g0Y6jRD17obhoo182US2d69pioHhfHv+ll8v4mPjy8Ul/oLy5CDZ1F+tb2rq6tFAYCcyHFCu3DhQpo3b86KFSvydO3dx9Hr9QwYMIBz586xfv16PD09zft8fX0xGAzExMRY1DyLjo6mUaNGBRZjdmS1xtYhCEKBkSSJUg4yoWUd6VXWNHkoRW/kxH0dR6O1HI7SciRaS1Tq46sppBngYJSWg1Fapp1M5JXyjgyt7MxzruLydkGSvfzQvjQQbbd+qI7uQb1jLcpLp62Ok4xGVEd2ozqyG2NgELrQruiatAFH5zyLZVlrz8cfJAjCMynHnwxxcXF07dq1QJNZnU7Hm2++yfnz51m/fr3VWsU1a9ZErVaza9cu87rIt2/f5uLFizRo0KDA4hQEIWuOKgVN/O1o4m+6XCzLMuFJBnOCezRay+kHOnTZ5LjJepnvzifz/flk2pWyZ1gVZ5r6a0T924KkUpvq0zZsjeLWNdQ7/kS1fytSuvVlQUXkTex+/QrNikXoG7+ALrQbxtJlnzqE9qVzV0lHyH+yLIu/QyFPZbc6WXZynNDWqVMnz8eeJiUlce3aNQCMRiMRERGcPn0aDw8PAgIC6NevHydOnGDZsmVIkmReqczV1RUHBwfc3Nx47bXXmDRpEj4+PuayXVWqVDGvqywIQuEiSRJBLiqCXFT0eM7Ui5uqlzkdYxqicDRay5EoLZEp1hmuDGy6lcamW2lU81QzrIozL5ZxEMs5FzBjqedIf2M06b0Go963FdWOtSgjb1gdJ6Wnod61DvWudegr10bbYwDGclWsTygUSU5OTsTFxeHu7i6SWiFPyLJMXFwcLi65nwyf40lhFy9epGfPnkyYMMFcIutphYWFmQsUP6pPnz6MHTuWGjVqZHq7RxdgyFhYYeXKlRYLK2QsO2drz+K4qsJCtL1t5FW7RyTpWX41le/OJ3E3myEKfg4KBlR04s2KTng/4/Vubfaal2WUF06i2rEW1fEwpGyW4tTXbGRKbEuXy/XdLLmYDMAbIYVrBTN4dt9v9Ho9ycnJNo0hISEBV1dXm8bwrMqPtndycjKvNZAbOU5oGzRoQHx8PFFRUTg4OBAQEGC1cIEkSRw8eDDXQRRnz+qbXGEg2t428rrdtQaZNTdS+eafJE7FZL1Mr70SXi5rGmdbyaPghkYVJoXhNS/F3ke1ewPqXetQxN3P8jhd/VZoX+yPHFA6x+cuzEvfFoa2f1aJtredwtT2OU6Bvb298fHxoVy53H+rFgRBeFIapUSvso68/JwD++9p+eafJDaGp/Hfb+JpBvj5Ugo/X0ohNNCOYVWcaV3CTlwKLWCyhze6bv3QdeqL8uR+NNtWobxgvRyn+vAuVEd2o2/SxrTQjE/AY8/dr4JYpUwQhMzlOKHdsGFDfsYhCIKQLUmSzJPLrifoWXguiaWXU0jKZGGHnZHp7IxMJ8RNxdAqzvQq64iDSiS2BUqlwlC3Oal1m6M8fwLNyu9RXvnH4hBJNqLeuxnVge3oWnZC1/lVZA/vLE4Ic5t45HfUgiAUUaKwoyAIRU4ZVxXTG7rzTy9/Pq3nSkmnzMfOXozXM2p/HFX+uMtnxxK4l5L12E4h/xgq1SJ1wtekvjsVQyZjZyWDHs2OP3F8/xU0y76BxLiCD1IQhCItVwntgwcP+Oyzz2jbti21a9fm8OHD5u3Tp0/n4sWL+RKkIAhCZtw0CkZUdeHkS34saelJfZ/M6z4/SDcy63QidVffY9G5JAzGJysLIzwFScJQsxGpHy8idfhkjJmMnZV0WjSb/8DpvT5oVv8AKUkW+++kGLgjvpQIgpCJHCe0N2/epGnTpnz99dfodDpu3LhBaqppXXZPT09Wr17N999/n2+BCoIgZEWlkOhWxoGtnXzY3smHF8s4kFklr0SdzAeH4mm3MZp/HmQ9wUzIRwoFhvotSZnyA2lvjcXo7W91iJSWimbtzzi91wf1+qWQbvqsqbT8LpWW3y3oiAVBKAJynNBOmjQJWZY5ePAgK1assCp826FDB3bv3p3nAQqCIORGXR8NP7T05ORLfrxT1RlXjXVmeyRaR4u/ovjsWAJpmYzBFQqAUoW+aTtSpv9CWr93Mbpbj52VkhOxW/Edju+9gnrrSoI0evwdxEg5QRCs5fid4e+//+att94iODg401nDQUFBREZG5mlwgiAIT6qUs4pP6rlx7mV/Pqvniova8n1LL8Os04k0WXuPsDvpNopSQKVGH9qVlJlLSe8zDNnFzeoQRUIsdku/5vLR97nifwz0ehsEKghCYZbjhDY9PR13d/cs98fHx6NQiG/OgiAULs5qBW9XdeFgdz/al7K32n81wUDnzfd5e28ssenZrL8r5C+NHbp2L5M8cxnpL76J7Gi9eILiQRT2P87CcUJ/lKdEzXNBEP6V4wy0UqVK7Nu3L8v9GzZsoHr16nkSlCAIQl4r4aTkt9ae/NzKM9PL1r9eTqH+6nusupbyxGuJC3nAwRFd19dJnrkMbae+yBrrLyGKO7dw+HIs9rM+QIq8aYMgBUEobHKc0A4dOpQ1a9Ywa9YsYmNjATAajVy6dImBAwdy9OhRhg8fnm+BCoIgPC1JkugS7MDB7n68mcnyqdFpRgbsjuXlbTGEJ4nL2jbl7Iq251ukzPoNbZuXkNXWq7+pzhzGcXx/NL9+BUkJNghSEITCIscJbc+ePfnoo4+YPn069erVA6BHjx40bNiQP//8k48//pj27dvnW6CCIAh5xd1OwZeN3dnUwZsQN+v1ZbbdTqfhmijm/yNKfNma7OaJtu/bpExfymL/lhixHAstGY1otq3G6YNXUW9bDQbxRUQQnkVSXFxcrt6tIyIi+Ouvv7h27RpGo5EyZcrQuXNngoOD8ynEoq0wrXP8rBFtbxtFrd3TDTKzTyfy5elEtJkMoa3lrWZuY3eqe2Ve47YwKWptn1sn72txirhC9Y3forxovZwugCEwGO0rwzFUq1egsRX3ti/MRNvbTmFq+xwtfZuSkkKvXr3o1asXr776KsOGDcvvuARBEAqEnVJibC1XupdxYNT+OA7c01rsP3FfR6t10bxdxZkxtVxwVInJr7ZS01sD3pVJrTEH5dE92P2+AMV9y7q0ysgbOMx6H33NRqaqCf6lbBStIAgFKUfvzI6Ojpw6dQqDQazQIghC8RTirmZDe2/mNHa3ql1rkGHu2SQa/xnFrttpNopQMJMkDPVakDL1J9JfegvZznrimOrkARw/fAPNb/MhOdEGQQqCUJBy3NXQuHFj9u/fn5+xCIIg2JRCkngjxInD3f3oGmydJN1INNB9awyD9zwgJk18wS9oU08kMPXEI5O/NHboOvclZcZSdE3bWR0vGQxotqzAacyrqHauBaN4zgShuMpxQjtjxgyOHTvGRx99xI0bNzAaRb1GQRCKJ39HJT+18uK31p6UcFRa7V9+NZVGf0ZxOEosyFCQpp9MZPpJ695W2d2L9LfGkjJ5IYbyVa32S4nx2P80G4eP3kJ57nhBhCoIQgHLNqFdtmwZN2+aavzVr1+fW7duMX/+fGrXro2vry8BAQEW/wIDAwskaEEQhILQobQDB7r7MqiSE/9dHzEq1UinTfdZdiXFJrE9i8bUdGFMTZcs9xvLVCR1/DzShn6E0dPXar8y4hoO00djP3cC0r3b+RmqIAgFLNtJYcOHD+fbb78lKCiI7t27Z7rkrSAIQnHmqlEwo6E7PZ9zZOS+WM7F/VsWSmuEoWGxXIjVMbGOK0qFeI/MT+NquT7+IElC37A1+lpNUG9ajmbDMiSt5bhn1fG9KE8fQtemB9our4GDdU1iQRCKlmwT2kdXy1mwYEG+ByMIglBY1fPVsLurL58fT2D2mSSLfXPPJnEhTsd3LTxx1YgqCIWCnT26bv3QN2+PZsV3qPdvs9gt6XVoNv6OKmwz2u5voG/RCVQ5KvwjCEIhJN55BUEQckitkJhU143vmntg95+htVsi0mmzIZobiaKwf345eV/Lyfvaxx/4CNnTl/TB40n5aD6GspWs9isS47D/eQ6O4/ujPBYGYtljQSiSHpvQimEGgiAIlnqWdWRjex/8HSzfQi/E6QldF03YHTFZLD+0XBdNy3XRT3RbY7kqpE6YT9qgDzF6eFvtV9y9hcNXH+Hw+Tsorp572lAFQShgj72+Mnz4cEaMGJGjk0mSRGRk5FMHJQiCUNjV8dGws7Mvr+yI4WSMzrz9QbqR7lvuM6uRO2+EiLGZeamGl/rpTqBQoG/SBn3dZmg2/I5603Kr8bXKS2dw/GQYuvqt0PZ8C9lXTHYWhKLgsQltnTp1xLK2giAImQh0UrKxgzdv741j9fVU83a9DKP2x3EuVsfn9d1QiclieWJ3F+vKBU/EzgHti/3RteqMZs2PqPZsQpItS1GqD+9CdSwM3fPdTRPHnHMwIU0QBJt5bELbv39/evbsWRCxCIIgFDmOKgWLW3hQyV3FlBOWNVIXnU/mUryeJS09cbcTUxYKG9nDm/Q330fXpgeaPxahOnXQYr9k0KPZsgJ12Ca0nV9F93x30NjZKFpBELIj3mEFQRCekiRJvF/TlZ9beeKosuyN/Tsyndbro7gUp8vi1oKtGUs+R9roaaR+8AWGoPJW+6WUJOyWL8Rx3OuoDmwHsbCQIBQ6IqEVBEHII12CHdjcwZuSTpYlEK4mGHh+QzQ7bqdlcUshJyr+foeKv9/Jt/MbqtQhdfK3poljmSzMoLh/D/uFn+HwyVAUF07mWxyCIOSeSGgFQRDyUHUvDTs7+9DAV2OxPUEr03NbDAv+SbKo8S3k3N1UI3dT87l39OHEsZTpv5D+8iDkTBZdUF6/iOPUUdjP/hAp8mb+xiMIQo5kO4Y2Nja2oOIQBEEoNnwdlPzVzpt398fx2yNL4xplGHc4nvNxOmY1dEejFJPFcuN8L/+CuzONHbqOr6Br3gHN2l9Q7/wTyWCwOER1cj/K0wfRt+iIqkaLgotNEAQrYlkUQRCEfGCnlJjf1J1KHiomHkng0T7Zny+lcDlezy+hnnjbK7M8h2ApwNEGbeXijvbVEehe6I7diu9QHdltsVsyGlHvWkflfVuRq9XHUKosxtJlMZYqi+wTAKKWuyAUCJHQCoIg5BNJkhhR1YUQNzUDdz8gQfdvWnvgnpZW66L5vbUXVTyfsr6qkO9kv5Kkvf0xistnsft9Acor/1jsV2rT4VgYqmNh/97G3hFjqecwlC6HsdRzGEuVxVjqObBzKOjwBaHYEwmtIAhCPmtTyp6tnXzosz2G64n/Xra+lWSg7YZoPq7nSofSDrbpgSxCRu4zDYOb28TDZjEYy1cldcLXKI/uwW7FIhT3bmd5rJSWgvLyWZSXz5q3yZKE7BuIsVTZh4muKcmVvf1Fb64gPAWR0AqCIBSAiu5qdnTyod+uB4Td1Zq3J+ll/ncgnv8diKeyu4rQEvaElrCjkZ8dDiqR4Dzqp0um8ci2TGgBkCQM9VqQUqsx6l3rUK/9GUViXM5uKstI926juHcb1dE95u2yoxPGkmUxlC6LseRzyH4lMPoEIHv6gFJ8VAvC44i/EkEQhALiaa9kdVtvxh6KZ/GFZKv95+L0nItL4ut/krBXQmM/O0JL2NG6hD0V3VVIz3gP3pzG7rYOwZJKje6FF9G16sztfbsIQofi1lWU4VdR3LqClGL9HGdFSklGeek0ykunLbbLSiWypx9GH39kn0BTkuvjj/Hhz7i4iZ5dQSAXCe3Fixe5cuUKHTt2NG/bt28fX3zxBfHx8fTo0YNhw4blS5CCIAjFhVoh8UUjdyq5qxhzKB5DFhW80gywMzKdnZHpTDiSQKCjglYl7GkdaEfLQDs8n8HJZG+EWJfQKhRUalICy6Av/8iiDLKMFHMPxa1rKMKvoLx1FUX4VaSo20i5KNsmGQxI0ZEooiOB41b7ZXsHjN4ByL4BD/8PNCW/3gGmhNfOPg8eoCAUfjlOaCdMmIAkSeaE9vbt2/Tq1Qs7Ozt8fHyYMGEC7u7uvPLKK/kWrCAIQnExsJIzLQPtWHEtlZ230zh2X4cxmzwnMsXI0sspLL2cggTU8lYTGmganlDPV4NaIXrpChVJQvb2x+Dtj6FWY8zrxKWnooi4jiL8qqk399ZVFLeuIaXmvDfX4m7SUlFGXIOIa5nuN7p6ILt7mf65eZr/Nz76u7tX0VzSNykexd0InMJvQKkSYO9o64gEG8pxQnvq1CmGDx9u/n358uUYjUb27t1LQEAAffr04fvvvxcJrSAIQg6Vc1MzrpaacbVciUs3svtOOjtup7HzdjoRyYYsbycDx+/rOH5fx6zTibioJZoF2FFNreLNEgb8iunksk3hqQC0L12EqwTYOWAsWxlj2cr/bpNlpPt3UYRfMSW6d2+hiI5Eir6LIuHp6sErEmIhIRbCr2R7nOzghOzuidHNOvmV3T2R3bwwunuCowsoCnBNpqQEFPciUNyNQBF12zT+OOPn5EQAKgDy0tkYy1dBX7Uehip1MQaXB0Xx/DsQMpfjhDY+Ph4vLy/z79u2baNZs2YEBAQA0LZtWyZOnJj3EQqCIDwD3O0UdA12oGuwA7IsczlebxpycDuNvXe1pOiz7r5N1MlsDE9jIxq+vH6X7sEODK7sTB0fTZa3KYr67HgAQFz/EjaOJI9JErJPAAafAAx1mlnuS0tBcf+uKbmNjkSKvoMi6g7S/Tsoou8gpefNcspSajJSajKKO7eyPU6WFMjOrsgu7uDihpzxzznjZ/d/f3d1R3Z2e/ywh+REU5J6L+LhhLkIFA8nzknJCTmL36BHeeEUygunYOX3yE6u6KvUwVClDoZq9ZC9/HLYEkJRleOE1sfHh/DwcADi4uI4evQon376qXl/enp63kcnCILwDJIkiQruaiq4qxlS2Zl0g8zBe1p23k5jR2Q6Zx/osrytzgh/XEvlj2up1PFWM7iyM92CHYrFqmRtSz2D40HtHTGWfA5KPodVn70sIyXGWSe50Q//j7mHZMzbpYIl2YiUGAc5rOoAIGvsTImu878JMIAi6mHSmpSzpDVXcSYnoD68C/XhXQAYA0qhr1IXQ9V6GCrWBAcxPKG4yXFC26pVKxYtWoSrqyt79+4FoEOHDub9Fy5coESJYvatWRAEoRCwU0q0CLSjRaAdHwP3UgzsikxnZ2Qau26nE52WedJy7L6OQXtimXAknv4hTvQPccK/CA9HWP681+MPepZIErKrB7Krh+UQhgwGPVLcA6S4GKT4GKT4ByjiYh7Z9sC8/b/L+uZpmNp0pJh7EHMvT88rqzUYfQPRpaRgHxuV7bGKO7fQ3LkF29cgK5UYy1ZBX9WU4BrLVMj98ASjAZISUSTEPkzw461+xmDA6OGN7OWH7OmL0cvH9LO7lyjFlg9y3KITJ07kypUrfPTRR2g0Gj755BNKly4NQFpaGn/++Scvv/xyvgUqCIIgmPg5KuldzpHe5RwxyjJnH+jYfjudJf/EEZ5mPb4xKtXI9JOJfHk6kW7BDgyq5ExdH/UzXwas2FOqkL18kb18sz/OaDSNVY1/JPnNSHjjHibB8TFI8bFIaSkFE/tDslqN0acEsn8JjH4lMfqVNNXo9SuJ7OENCgWXL1+mgrszyn+OoTp7BOU/x5BSkrI8p2Qw/FsibfUPyE4uGCrXRl+1HsYyIabbJsYjJcaZEtPEeKSEOBSJsZAQb/o/KSFX1SosHpOkMI1L9vLD6OmL7Onz788ZSa+LuyjHlktSXFxcrp6R+Ph4HBwc0Gj+HZuVmprKlStXKFmyJB4eNi54XchcvnyZ8o+WchEKjGh72xDtbjsXL10m3LEUi84lse129sPAanmrGVzJme5lHLArBsMRbO2Zed3rtEhJCUgPEz4pKR4pMd6cAGb8/u+/OCSDPttTyiq1qdyYX0mMfiUw+pdE9n34v4fPYyehWbW90YDi+kWUZ4+iOnsUxdV/8rUHOj/IajWyhw/GjN5dvxLoG7ZG9itcV8IL0+s+133ebm5uVtscHByoVq1aru983759zJs3j1OnTnHnzh3mz59P3759zftlWWbatGn89NNPxMXFUadOHWbNmkWlSpXMx8TFxfHBBx+wefNmANq1a8eMGTNwd3fPdTyCIAhFmUKCF0ra80JJe67E6/jufDK/XUkhUWfdb3Hivo4hYbF8dCSeN0KceLOiU6Ffetf9R9Mys8VuUlhRotYge3ibekdzQpYhLcUywU2KB73etECEX0nTamh5WZFAoTRXktB1fR1SU1BeOIny7BFTgns3+4lvhYGk0yFFRaKIijRv06xZgr5eC3QdemMsE2LD6AqnXCW0V65cYcaMGYSFhXH//n1WrlxJixYtiImJYeLEifTv35+6devm+HzJyclUrlyZPn36MGTIEKv9c+fOZf78+cyfP5/y5cszY8YMunfvzpEjR3BxcQFg4MCBREREsHLlSgDeeecdBg8ezPLly3Pz0ARBEIqVcm5qpjd0Z0IdV5ZdTmHR+WSuJFj3lEWnGZl5KpHZpxPpGuzAoEpO1PfViOEIQt6QJHBwMpUF8w20TQwOjhhqNcZQqzFaQLp/F+XZo6Ye3HPHzOW/ckt2dDZNdnN1f1jd4eHPD39HoUB6EI0UE4XiQZRpkt6D6CcuxSbJRvNEN33l2ug6voKhSh0xNOGhHCe0Z86coWPHjtjZ2dGoUSPWr19v3ufl5cX58+dZvHhxrhLaNm3a0KZNGwCrVcZkWWbBggWMGjWKrl27ArBgwQLKly/PypUr6d+/PxcvXmT79u1s3ryZ+vXrAzB79mzat29fqLrBBUEQbMVFrWBQZWcGVnJiV2Q6355LYmuE9XAEvQyrrqey6noqNb3UDKrkRPcyjjioCs+HpeiZFfKC7O2PvmUn9C07kW40oLhxGeXZIyjPHUcRF4Ps5PpvkuqaScL6sDQZKvWTBaBNR4qNRvEw2ZVi7pl+fvDIz49ZaEN17jiqc8cxBJVH17EP+rrNn/mJZjl+9B9//DF+fn5s374dvV7PunXrLPa3bt2aVatW5VlgN2/e5N69e4SGhpq3OTg40LhxYw4dOkT//v05fPgwzs7ONGjQwHxMw4YNcXJy4tChQyKhFQRBeEghSbQuYU/rEvZcS9Dz3fkkll5OISGT4QgnY3QM2xvH/w7E0zLQjval7Wlb0r7YLtggPMMUSozPVcT4XEV0XV4rmPvU2CH7lcTgVzLrY1KSHvbqRqO4F4Fq93qUEdetDlPevIzym08w+gSibf8y+mbti+aqb3kgxwntwYMHGTt2LG5ubjx48MBqf6lSpbh7926eBXbvnqm8h4+Pj8V2Hx8f7ty5A0BUVBReXl4Wl8YkScLb25uoqKxLeFy+fDnP4syJgr4/4V+i7W1DtLvt5LTt3/SEXnVgY7SKPyJV3Ei1nniTapDZdCuNTbdMxfuruhho5mmguaeBso6yuNL5H+J1bzvFtu0dPaGMJwRXw/XqWXz3b8Yl/JLVYYroSOx/noNu5WKi64Vyv24rDA5OBRJiQbZ9dh2VueqftrPLOuuPiorKdn9hUpA9t2Log+2ItrcN0e628yRtX7MSjJNl/o5MZ+H5ZLbeSiOr0jdnE5WcTVSy4CaUdlbSrpQ9HUrb09jPrkAWbui1PQYonPVoxevedp6Ztq9QAdq/SMqVf9Bs/B3l8b1WpcPUKYkE7l5LwMEt6Fp0Qtfu5ceXbXsKhantc7wgc40aNdiyZUum+3Q6HatWraJevXp5Fpifn2mZuujoaIvt0dHR+PqanhxfX19iYmKQH3lCZVnm/v375mMEQRCE7EmSRKsS9ix/3ovjPfwYXsWZkk7ZDy8ITzKw6Hwy3bbEUG7ZHfrvesAfV1OITc/blaketeVWGltu5c1Sr4JQVBnLVSHtnU9JmfoTuuYdkDMZyyulp6HZuhLH9/tgt2gqiohrNoi0YOU4of3f//7Hzp07eeeddzhz5gwAd+/eZfv27XTp0oUrV64wevToPAssKCgIPz8/du3aZd6WlpbGgQMHzGNm69evT1JSEocPHzYfc/jwYZKTky3G1QqCIAg5U8ZVxZT6bpzp6ceeLj6Mq+VCLe/sJ78k6GTW3Ehl0J5Yyi27Q8dN0Xx9NpGr8dnXH82tZa09WdbaM0/PKQhFlRxQmvQBH5AyaxnaDn2QMxliIBkMqPdtwXH8m9h/ORbFxdOmUmrFUI6HHISGhvLtt9/ywQcf8OuvvwIwdOhQZFnGzc2NRYsW0bBhw1zdeVJSEteumb41GI1GIiIiOH36NB4eHpQqVYqhQ4fy5ZdfUr58ecqVK8esWbNwcnLipZdeAiAkJITnn3+ed999lzlz5gDw7rvv0rZt20LTBS4IglAUSZJEdS8N1b00jKnpSmSygS230th8K5W/76STnkWdeoMM++5q2XdXy4QjCVRwU9G+lD1vVXKipPPTzcJuX9rhqW4vCMWR7OGNttdgtJ37ot61DvXWlSjiYqyOU506iOrUQQzlqpDeZxjGclVsEG3+yfVKYSkpKezatYurV69iNBopU6YMoaGh5rqwuREWFkbnzp2ttvfp04cFCxaYF1ZYsmSJxcIKlSv/u2Z1xsIKmzZtAqB9+/aFamGFwjS+5Fkj2t42RLvbTkG1fbLOyN+R6Wx6OAQgOu3xwwxc1BKr2nhR37dozLXILfG6tx3R9v+h06LatxXNpuVZLiIhKxRoX3oLXftej12JLTuFqe1zndAKuVOYnuxnjWh72xDtbju2aHujLHMsWsemW6lsCk/jfFzWwwxc1RJr2npTx0eT5THZWXLRVJvzjZCCmb2dG+J1bzui7bNgNKI8vg/Nxt9QXj2f6SH6Gg1JGzQOnK1Xgc2JwtT2T56WC4IgCM88hSRRz1fDxDpuHOjux8mX/Jha343mAXb8d02GBJ3Mi1vvc/K+9onua9T+OEbtj3v6oAXhWaBQYKjbjNSPviFl3Fz0NayHhapOHcTxo7dQXPnHBgHmrSwHNHl4eOR66UNJkoiJsR63IQiCIDwbgl1UDK3izNAqzsSlG5l7JpHZZ5LM++O1Mt233uevdj5U88zdSkv9KjjmdbiCUPxJEsaKNUirWAPlyQPYL5qKlJxg3q14EIXD5++g7TkIXbuXi+xSulkmtB988IFYy1sQBEF4Yu52CibWcUUpScw6nWjeHpsu023zfda196ayR86T2rlNPPIjTEF4ZhhqNiLl0++w/+YTlI/0ykoGA3a/L0B54RRpb40FZ1cbRvlkskxox40bV5BxCIIgCMWQJEmMr+2Czigz9+y/PbUx6Ua6br7P+vbehLjnrqdWEIQnJ3v5kTpuLpqV36HZtNxin+rkfhwnvUXasEkYy1bO4gyFkxhDKwiCIOQrSZKYXNeVoZUtJ3NFpxnpsvk+V+J1OTrPnRQDd1KyqBcmCELOqVRoew8lddTnyE6WVaoU9+/hMOUd1FtWFKmatbkuChgZGcmpU6dISEjAaLQu1dKnT588CUwQBEEoPiRJ4vP6buhl+O58snn7vVRTUruhvQ9lXLP/SKq0/C4Acf1L5GusgvCsMNRqTMon32E//2OU1/6thCAZ9Nj9Nt80BGHgGHDKfWnWgpbjhDY9PZ3hw4ezZs0ajEYjkiSZl5x9dKytSGgFQRCEzEiSxIwGbuiNMj9eTDFvj0wx0vnh8INgl6w/lvwdxEVFQchrsrc/qeO/QvPHIjRbVljsUx3fi+PEK6QNn4zxuYo2ijBncvzuMGXKFP7880/Gjx/P+vXrkWWZBQsWsGbNGkJDQ6lWrRr79u3Lz1gFQRCEIk6SJL5o5M5r5S0rFkQkG+iy+T63krKuY3uhdwAXegfkd4iC8OxRqdG+MpzUdz5FdrQcGqS4fxeHz95GvW11oR6CkOOEds2aNfTu3ZvRo0dTqVIlAAICAmjZsiUrVqzA0dGRH374Id8CFQRBEIoHhSQxt4k7vctaLmUbnmSg8+b7RCaLcbKCYAuGOs1I+eR7DGVCLLZLBj12v36F/deTICUpi1vbVo4T2qioKOrVqweASmW6JJSWlgaYvnF37dqVv/76Kx9CFARBEIobhSQxv6kHPZ+zTGpvJBrovDmau2LylyDYhOwTQOr4eWhfeNFqn+roHhwnDkJx45INIstejhNab29vEhJMhXhdXFxwcHDg+vXr5v06nY7k5OSsbi4IgiAIFpQKiQXNPOgWbJnUXk0wDT+ISrVMalv8FUWLv6IKMkRBeDapNWhffYfUtycjO/xnCEJ0JA6fDke1489CNQQhxwlttWrVOHbsGGDqkW3SpAkLFizgwIED7Nu3j0WLFlGtWrV8C1QQBEEoflQKie9aeNCptL3F9kvxerpuvs/9tH+T2lMxOk7F5KzElyAIT89QryUpHy/CEFTBYruk12H/8xyC1yyC1MLRmZnjhLZfv37o9XrzMINPPvmEpKQkOnbsSKdOnUhJSWHKlCn5FqggCIJQPKkVEj+09KRdKcuk9nycKal98DCp/buzD3939rFFiILwzJL9SpA6YR7a1t2s9jlG3oBMSrjaQo7LdrVv35727dubf69UqRInTpxgz549qFQqGjZsiLu7e37EKAiCIBRzGqXET608eXVHDNtup5u3/xOrp9uWGP5q501Nb40NIxSEZ5jGDu3rozCGVMfuh1lIaSnIKjXXXxxMyUJSo/apivq5urrSqVMn2rVrJ5JZQRAE4anYKSV+CfWiVaCdxfbTD3S8uPU+8drC0RMkCM8qfYNQ0xCE0uVMK40FBts6JLMnTmjDwsIYMWIEPXv2ZPz48dy6dSsv4xIEQRCeQfYqiaWtPWnmb9kbe/y+jkZr7jH5aLyNIhMEAUD2L0nqxG/QPd/d1qFYyDahnTZtGgEBAdy/f99i+9KlS+natSu//vor27dv55tvviE0NJTw8PB8DVYQBEEo/hxVCn5/3otGfpZJbWSKkTlnkkjSiZ5aQbAptQYeWSW2MMg2oQ0LCyM0NBRvb2/ztvT0dMaNG4erqytr164lIiKCH374gaSkJL788st8D1gQBEEo/pzUCv54wYsGvtbjZntvjyFVX3jKBQmCYHvZJrTXrl2jZs2aFtt2795NYmIi77zzDs2bN8fJyYnu3bvz8ssv8/fff+djqIIgCMKzxEWtYMULXtTxVlts33tXS79dMWgNIqkVBMEk24Q2NjYWf39/i21hYWFIkkTbtm0tttesWZO7d+/mfYSCIAjCM8tVo2BVG29qelkmtVsj0hm8JxaDUSS1giA8JqH18/Pjzp07FtsOHDiAo6MjFStWtDyRQoFGI0qqCIIgCHnL3U7BmrbePOeitNi+5kYq7x6IQy5EqxUJgmAb2Sa0derUYdmyZcTFxQFw9uxZTpw4QYsWLVAqLd9YLl68SIkSJfItUEEQBOHZ5WGn4FqiwWr7z5dSmHAkQSS1gvCMy3ZhhTFjxtCiRQvq1KlDSEgIZ86cQZIkRo0aZXGcLMusX7+e0NDQ/IxVEARBeIbV8FKjNcgk6mQikv9Nbuf/k4SrRmJMTVcbRicIgi1l20MbEhLCX3/9RZ06dbh//z4NGjRg9erV1KtXz+K4sLAwnJ2d6dKlS74GKwiCIDy7dnfx5UB3P9a09cLb3vLja+qJRBb8k2SjyARBsLXHLn1bv359/vjjj2yPad68Ofv378+zoARBEAQhK+Xd1Kxu40WnzfdJ0P471GDc4Xic1RKvVXCyYXSCINjCUy19KwiCIAi2UN1Lw4rnvXBUWRZ3H7k/jj+vp9ooKkEQbEUktIIgCEKRUPH3O1T8/d/KOw387PittSeaRz7JjDK8tecB2yPSbBChIAi2IhJaQRAEoUi4m2rkbqrlsrctA+35oaUnykc6anVGeG3nA/bfTS/gCAVBsBWR0AqCIAhFwvle/pzv5W+1vVOQA/ObelhsSzXI9Noew4n72oIKTxAEGxIJrSAIglAkBDgqCXBUZrqvdzlHZjV0s9iWqJPpsTWGC3G6gghPEAQbEgmtIAiCUCwMrOTMxDqWtWgfpBvptvk+NxL1NopKEISCkKuEVqvV8vPPP/PWW2/RrVs3Tp06BUBcXBzLli3j9u3b+RKkIAiCIIzcF8vIfbHZHjO6ugujqjlbbLubaqTr5vtEJluvNCYIQvGQ44T2wYMHtGrVipEjR7Jnzx727NljXhLX1dWVKVOmsGjRovyKUxAEQXjG/XQphZ8upTz2uEl1XBlQ0bIW7c0kA9233CcmTSS1glAc5TihnTRpErdu3WLz5s3s37/fYt1shUJBly5d2LZtW74EKQiCIAhzGrszp7H7Y4+TJImZDd14+TkHi+0X4/X02BpDvNaYxS0FQSiqcpzQbt68mcGDB9OgQQMkSbLaX7ZsWSIiIvI0OEEQBEHI8EaIE2+E5GwVMIUkMb+ZBx1K21tsPxmjo/f2GFL0IqkVhOIkxwltYmIiJUuWzHJ/eno6BoO4lCMIgiAUDmqFxA8tPGkRYGex/cA9La/vfIDWIGdxS0EQipocJ7TPPfccJ06cyHL/zp07qVSpUp4EJQiCIAj/tSk8lU3huVvW1l4lsbS1J/V9NBbbt99OZ9CeWAxGkdQKQnGQ44S2X79+/Pbbb/zxxx8YjaZLNZIkkZKSwuTJk9m5cyf9+/fPt0AFQRCEZ1ufHQ/os+NBrm/nrFbwxwteVPVUW2z/80Yqg8NiSRBjagWhyFPl9MDBgwdz4cIFBg8ejIuLCwBvvvkmcXFxGAwGBg4cSN++ffMtUEEQBOHZ1raU/eMPyoK7nYLVbbzosPE+VxL+rUm78loq+++mM7OhOx2DHLI5gyAIhVmOE1qA2bNn07t3b9asWcO1a9cwGo2UKVOG7t2707hx4/yKURAEQRBY/rzXU93e10HJmrZetN94n4hHatJGphjpu/MBnYPsmdHQPcvVyARBKLxyldACNGjQgAYNGuRHLIIgCIKQr0o5q1jb1puXt9/naoLlROZ1N9PYHXmPyXXdeCPEEUUmFX0EQSiccjyG1tPTkxUrVmS5f/Xq1Xh6euZJUBkMBgOfffYZ1atXx8/Pj+rVq/PZZ5+h1/97uUiWZaZOnUrFihXx9/enY8eOnD9/Pk/jEARBEIqPsm4q9nb1Y3R1Z1T/yVkTdDKjD8TRYeN9LsTpbBOgIAi5luOE9tGFFDJjNBozrU/7NObMmcP333/P9OnTOXz4MNOmTeO7777jyy+/NB8zd+5c5s+fz/Tp09m5cyc+Pj50796dxMTEPI1FEARBsC33H2/j/mPeLLHuoJKYWMeNv7v4UsdbbbX/YJSWZmuj+PxEAumivJcgFHo5TmiBbBPWo0eP4u7u/rTxWDh8+DDt2rWjffv2BAUF0aFDB9q3b8+xY8cAU5K9YMECRo0aRdeuXalcuTILFiwgKSmJlStX5mksgiAIQvFT1VPN1o4+TG/ghvN/umt1RphxMpGma6PYdzfdRhEKgpAT2Sa0CxYsoEaNGtSoUQOAcePGmX9/9F9wcDCLFi2ibdu2eRpcw4YN2bt3L5cuXQLgwoULhIWF8cILLwBw8+ZN7t27R2hoqPk2Dg4ONG7cmEOHDuVpLIIgCIJtxfUvQVz/Enl+XqVCYnBlZw529820ksLleD0dN91n5L5Y4tJFiS9BKIyynRTm4+NDxYoVAQgPDycgIICAgACLYyRJwsnJiZo1azJw4MA8DW7UqFEkJSXRoEEDlEoler2e9957z3w/9+7dM8f537jv3LmT5XkvX76cp3E+TkHfn/Av0fa2IdrddkTbP51PS0NLJyUzr2p4oLPssf3pUgrrryfzXlktrb0M/PeipWh72xFtbzsF2fbly5fPcl+2Ce1LL73ESy+9BECnTp14//33adGiRd5Gl43Vq1fz+++/8/3331OxYkXOnDnD2LFjKV26NK+//voTnze7Bslrly9fLtD7E/4l2t42RLvbjmj7vFEB6FPbyKSj8fx0KcViX4xOYtwFO9qWsueLhm6UdDZ9jIq2tx3R9rZTmNo+x2No+/btS3BwcJb7b968ybJly/IiJrOJEyfy9ttv06NHD6pUqULv3r0ZPnw4s2fPBsDPzw+A6Ohoi9tFR0fj6+ubp7EIgiAIttVrewy9tscUyH252ymY28SDDe29Ke9m3fez5VYaDddEsfBcklg+VxAKgRwntMOHD+fw4cNZ7j927BjDhw/Pk6AypKSkoFRaFrhWKpXmpXeDgoLw8/Nj165d5v1paWkcOHBA1MoVBEEoZrbcSmPLrbQCvc8m/nbs7erLBzVdUP/nEzNJLzP2UDxtNkRzOVnUrBUEW8rxwgqPK9uVmppqlXw+rXbt2jFnzhyCgoKoWLEip0+fZv78+fTu3Rswjd8dOnQoX375JeXLl6dcuXLMmjULJycn81AJQRAEoXhY1jpva53nlJ1S4sNarrxYxoFR++I4GKW12H/svo7XYuwZr0hkdHUXm8QoCM+6bBPaW7duER4ebv790qVL7Nu3z+q4uLg4fvzxR4KCgvI0uBkzZjBlyhT+97//cf/+ffz8/OjXrx8ffPCB+ZiRI0eSmprK+++/T1xcHHXq1GH16tW4uIg3FUEQhOKkfWkHm95/RXc1Gzt4s+RiCpOPxpOg+7ejxyBLfHIsAU87BW+EONkwSkF4NklxcXFZdr1OmzaN6dOnP3bBBFmWUSqVfPXVV7zyyit5HmRRVpgGTD9rRNvbhmh32xFtX3Aikw2MORTHupuWQyDUCtjQ3pv6vnY2iuzZI173tlOY2j7bHtru3btTqVIlAN544w0GDx5Mo0aNLI7JKNtVvXp1q/JZgiAIgpBXllxMBigUPaCBTkp+CfVi/c1UBu+JJVlv6hvSGeH1nQ/4u4sv/o55OwxPEISsZZvQhoSEEBISAsD8+fNp3LhxtpUOBEEQBCG/jNofBxSOhDZDpyAH5jeFN/5+YN52N9XIG7se8Fc7bzRKMVlMEApCjqscvPLKK+Zk9urVqxw8eJD4+Pj8iksQBEEQLPSr4Ei/Co62DsNKtzIOvF5SZ7HtYJSWDw+Lz0hBKCg5TmgBVqxYQdWqValXrx4dOnTg5MmTAMTExFCnTh3WrFmTHzEKgiAIAnObeDC3iYetw8jUsCAdoYGW42a/v5DML5eSbRSRIDxbcpzQrl27lkGDBlGhQgU++eQTizJeXl5eVKhQgd9//z1fghQEQRCEwkwpweKWngQ5W46b/d+BOI5Fa7O4lSAIeSXHCe0XX3xBy5YtWb16daaVDOrWrcvZs2fzNDhBEARByHAnxcCdFIOtw8iSh52CX1t74fDIuFmtEV7bGUNUauGNWxCKgxwntJcuXaJTp05Z7vfx8eH+/ft5EpQgCIIg/Fel5XeptPyurcPIVjVPNfOaultsi0wxTRLTiSVyBSHf5DihdXR0JDk567FA169fx8vLK0+CEgRBEIT/8ndQ4O+Qq6kfNvHSc468XcXZYtv+e1omiEligpBvcvzO0Lx5c3777Te0WuuxQHfu3OGnn34iNDQ0T4MTBEEQhAwXegdwoXeArcPIkcl1XWkeYDlJ7NvzySy7kmKjiASheMtxQvvRRx9x9+5dWrZsyffff48kSWzbto3JkyfTuHFjFAoFY8aMyc9YBUEQBKFIUCkkfmzpQan/TBJ7d38sJ++LSWKCkNdynNCWLVuWLVu24Ofnx7Rp05Blmfnz5zN37lyqVavG5s2bKVWqVH7GKgiCIAhFhpe9kl9aeWL/SE6bZoBXdz7gfpqYJCYIeSnblcIyGAwGIiMj8fX1Zc2aNcTFxXHt2jWMRiPBwcF4e3vnd5yCIAjCM67FX1EA7O7ia+NIcq6mt4Y5jT0YEhZr3haRbKD/rgesaeuNSiFWEhOEvJCjHlqj0UitWrVYunQpAO7u7tSuXZu6deuKZFYQBEEoEKdidJyK0T3+wEKmdzlHhlS2XK437K6WSUcTbBSRIBQ/OeqhVavV+Pv7I0nim6QgCIJgG3939rF1CE/s03punHmgY9/df8fPzv8niZpeanqWLXzL+QpCUZPjMbR9+/blt99+Iy0tLT/jEQRBEIRM1fTWUNNbY+swnohaIbGkpSclHC0nib2zL47TMWKSmCA8rRz10AKUK1cOo9FIvXr16NOnD8HBwTg4OFgd17179zwNUBAEQRCKAx8HJb+EetJ+UzTpD+eEpRpkXt35gL87++Bpr8z+BIIgZCnHCe2gQYPMP8+cOTPTYyRJEgmtIAiCkC+mnjCNOR1Xy9XGkTy52j4avmzkzvC9ceZt4UkG3twdy8oXvMQkMUF4QjlOaNetW5efcQiCIAhCtqafTASKdkIL0Le8Eyfv6/juwr+rb/4dmc6nxxL4uJ6bDSMThKIrxwlt06ZN8zMOQRAEQcjWmJoutg4hz0yp78bZWB0H7v07fnbu2SRqeqvpXkZMEhOE3Cr8i2ILgiAIAqae2aLeO5tBozRNEgtwtPwYHr43jn8e/L+9Ow+Pqrr/OP6eTPYQmOxhSVgTILJTEFktKIhUIAULFFuhtUGgFm1ZlSoirSCIIiIFF7CKlRQRgghqJSJiIBSh+AMNIBj2JIRMyL7N/P6IDI6BEDHJzCSf1/PwQO49M/ne8xzgMyfnnut6W5OJOFqVZ2gB0tPTeeONNzh48CCXL1/GYrHYnTcYDCQkJFRrgSIiInVRmK+Rf/48iGHbMij+7r/T/FIr43dkknhPKAFemnMSqaoqB9ojR47wi1/8gvz8fNq0acORI0do164dZrOZ8+fP07JlS5o2bVqTtYqISD128GL5j+dddeuua+kR6smS20z8abfZduzbnDK6brhATIAH7UwetDO50/a730N93LQnvMg1VDnQPvnkk3h7e5OYmEiDBg1o06YNTz/9NAMGDGDDhg3MnDmT1157rSZrFRGReuz2LRkAmCfWrcmT30b7ceBiMWtS8m3HzMVWPk8r5vM0+z1qA7wMFUJuO5MHYQq6Us9VOdDu2bOHqVOn0rx5c7Kyyp9JbbVaARg9ejR79uzhr3/9q3ZDEBGRGtE5yMPRJdSYhbeaOHyplOSMyh+ykFVkJSmt2O5mMoBGntcOuo19FXSlfqhyoC0pKSE8PBwAb29vALKzs23nO3bsyNtvv13N5YmIiJTbOTzU0SXUGC+jgXWDAvnjbjM7zxVSWPbjXp9dbGVvejF70+2DbqCXG79u48v0zv6YtCZX6rAqB9qIiAjOnDkDgI+PD+Hh4SQnJzNixAigfI2tn59fzVQpIiJSx4X4GFl/RxBlFiuncsv42lzC1+ZS2+9HzaUUlFl/1HteKrLw4uFc3jqez+wu/kxs54eHHt4gdVCVA22/fv3YunUrjz76KAD33nsvL730km23g/Xr1/Ob3/ymxgoVERGpD4xuBlo2dKdlQ3eGRl49brFeDbop5lJb2E0xl5JfWnnQvVRkYebebF75Oo+nejRicDMvLUWQOqXKgXbatGn069ePoqIivLy8eOyxxzCbzWzevBmj0ciYMWN46qmnarJWERGpx9q9fR6Ar8c2dnAljuFmMNDC350W/u7cFXH1uMVq5XRuGSnmUlLMJXz13e9fm0vJ+0HQPZpdypj/ZPLzJl78rWcjYgLq7rpkqV9+1JKDiIirf4O8vLx44YUXeOGFF2qkMBERke+7UGC5caN6yM1goLm/O8393Rkc4W07XlBqZcXhXJ47lFMh2CaeK6Lv5nTuj/bl0a4NCfEx1nbZItVKK8RFRMQlfDUmnK/GhDu6DJfh425gemd/9o8K4zdRvvxwgYHFCmtS8un2ThrPH8qh8AbLFkScWZVnaBctWlTpeYPBgLe3N02aNKF37940adLkJxcnIiJyRWNfzSLejHBfI8v7BvCH9n48lpzNrgv2OyHklFiZt/8yr6Xk8eTPGjKyhY/W14rLqXKgXbhwoW2AX9l/9oofHjcajdx///0sXrwYNzdNAouIiDhapyBPEu4KZtvpQv66L5tvLtvvDXYqt4yJn2SxKjSPv/VsRPeQuvNENqn7qpw2Dx8+TExMDOPGjeOTTz7h1KlTnDp1isTERMaOHUvHjh3Zv38/O3fu5N5772XNmjU8++yzNVm7iIjUI9N2ZzFtd5ajy3BpBoOBuyN9SBoZxt97NqKRZ8WZ2D3pxQx6L4O4nZc4k1vqgCpFfrwqB9rp06fTtm1bVqxYQefOnfH398ff358uXbrw0ksv0bp1a+bNm0enTp1YuXIlgwYN0oMWRESk2rx+NJ/Xj+bfuKHckKfRwJRbGnBgVBhx7f0wXmOFQfyJAnpsTOdvX1wmt0Q35Ilzq3Kg3bVrF3369Lnu+T59+vDJJ5/Yvr7zzjttD2IQERH5qZ7vbeL53iZHl1GnBHobeaaXiaSRoQz53g4JVxSUWVn8vxx+9k4a647lYbHqxjFxTlUOtJ6enuzbt++65/ft24eHx9X97EpLS/XkMBERqTYT2voxoa3+X6kJ0SYP1t8RxKYhQcQEVLy95kKBhamfmRm8NYMUc4kDKhSpXJUD7ahRo1i/fj2zZ8/m2LFjlJaWUlpayrFjx5g1axbx8fGMGjXK1n7Xrl20bdu2RooWERGR6nd7E292DQ9lWW8TId4VI8J/M0ron5DOc4dyKLVotlacR5V3OZg/fz4ZGRmsWrWK1atX2+1sYLVaGT58OPPnzwegsLCQLl26cOutt9ZM1SIiUu9sO1UAwNBIHwdXUrcZ3Qzc39aP2JY+PP9lDisO51L0vQ0Risrgyf2XSUgtYEXfAD1tTJxClQOtt7c3a9as4eGHH+bjjz/m9OnTQPkTxAYOHEiXLl3s2s6aNavaixURkfpr3MeXADBPbOrgSuqHhp5uPN69EfdH+zEnOZv3TxXanT9wsYQBCenM6tKQaR0b4OGmvWvFcaocaK/o3LkznTt3rolaRERErutaNy1JzWvu7866gYFsPFnAjD3ZXCq6uuNBiQUWfHGZhG8LeKlfAB0CNVsrjqGnHoiIiEtYf0cQ6+8IcnQZ9ZLBYGBUK1/2xIYyvHnFDxaHLpVwe0I6Cw9cprhMa2ul9l13hrZTp064ubnZdi/o1KnTDR+FZzAYOHjwYHXXKCIiIk4g1MfIPwcGselkAX9JMpP5vdnaUissPJjDe6cKWdHXROcgPWlMas91A22fPn0wGAy2R9de+bq2XbhwgXnz5vHRRx+Rm5tLixYtePbZZ+nbty9QflPawoULef311zGbzXTv3p0lS5bQvn37Wq9VRESkPhjZ0oe+jT2ZuSebjScL7M7936USBm3J4JFO/szo7I/ntZ7aIFLNrhtoV65cWenXtcFsNjNkyBB69epFfHw8QUFBpKamEhISYmuzbNkyVqxYwYoVK4iKiuKZZ54hNjaWffv24e/vX+s1i4hIzTCtOQvopjBnEext5LXbAxnZony2NqPQfrZ28f9y2JpawIp+AXQN1myt1CynXkP7wgsvEB4ezqpVq+jevTstWrRgwIABtv1trVYrK1eu5OGHH2bEiBHExMSwcuVKcnNz2bBhg4OrFxERqfuGt/Bhb2wo97aquJ3aEXMpd7yXwfz92RRpba3UoEoD7YULF+jRowcLFiyo9E0WLFhAz549uXjxYrUWt3XrVrp3787EiRNp06YNffv2ZfXq1Vi/e/ReamoqaWlpDBw40PYaHx8fevfuzd69e6u1FhERcSzzxKaanXVSgd5GXh4QyFuDAgnzsY8WZVZYeiiXAQnp7M8odlCFUtcZzGbzdT8yPfnkk7zxxhscOHCg0h/f5+Tk0LVrVyZOnMhjjz1WbcWFhYUBMGXKFEaOHMmXX37JrFmzeOKJJ4iLi2Pv3r0MGTKEL7/8koiICNvrpk6dyvnz59m4ceM13/fYsWPVVqOIiIhclV0Cz530ZGt6xVWNbli5r2kpcc1L8HLqnxGLM4qKirruuUr3of3www+JjY294VpUf39/Ro0axbZt26o10FosFrp27coTTzwBlO+Be+LECV555RXi4uJu+n0r65DqduzYsVr9fnKV+t4x1O+Oo753HPW9vXUx8MHpQh7+PIvz+VfX1low8M+zHiTl+jC9sz+Dm3kT8BOTrfrecZyp7ysdRSdPnqRDhw5VeqOYmBhOnDhRLUVdERYWZlsve0V0dDRnzpyxnQfIyMiwa5ORkUFoaGi11iIiIo415j+ZjPlPpqPLkCoaEuFN0sgw7ovyrXDuWHYpkz7Nos2/zjNsWwYv/l8O32SXOqBKqSsqDbQGgwGLxVJZExuLxVLt23r16tWL48eP2x07fvy4bXlB8+bNCQsLIzEx0Xa+sLCQpKQkbr311mqtRUREHOuD04V8cLrwxg3FaZi83HixbwAb7gyiqa+xwvkyK+y+UMzcfZfpvjGNnhvTeGJfNklpRZRZdBOZVF2lgTYyMpL9+/dX6Y2++OILIiMjq6WoK6ZMmcK+fftYsmQJJ06cYNOmTaxevZoHHngAKA/ckydPZtmyZSQkJHDkyBGmTJmCn58fo0ePrtZaRETEsf41KJB/DQp0dBlyE+5o5s3nsaHcH11xtvb7jmaXsuz/chn6/kWi377A5F1ZJHxbQG5J1SbXpP6qdA3tkCFDWLVqFX/605+Ijo6+brujR4+yYcMGHnzwwWotrlu3bqxbt4758+ezePFimjVrxqOPPmoLtADTpk2joKCAGTNm2B6ssHHjRu1BKyJSxwyNrLgtlLiORp5uLOsTwO/b+bH52wK2nSrkiPn6ywwyiyz863g+/zqej6cb9G/sxdBIb+6K8KGpX8XZXqnfKt3lIDMzk169euHm5sbf/vY3Ro4cibv71QxcWlrKpk2bmDt3LhaLhaSkJIKC9Jzt73OmBdP1jfreMdTvjqO+dxz1/c35NqeU7acL2XaqkN0Xiiit4iqDToEeDI30ZmiEN76XUomOVt87gjON+0pnaIOCgvj3v//NfffdR1xcHH/6059o06YNDRo0IDc3l+PHj1NYWEjjxo15++23FWZFRKTGrE3JA2BCWz8HVyLVpYW/Ow/GNODBmAaYiyzsOFvIttOFfHimkOzi66fbQ5dKOHSphEUHcwj19OaOtCwGNPGif2MvGl9jra7UfZUGWoAuXbrw+eefs2bNGrZv387XX39NTk4O/v7+dOrUiaFDhzJhwgQaNWpUG/WKiEg99fDnZkCBtq4yebnxy1a+/LKVLyUWK3vSitl2unxpwsmcsuu+Lr3YjbeO5/PW8XwA2jZyp38TLwY09qJvuBcmbXhbL9ww0AI0bNiQadOmMW3atJquR0RE5JpudEOR1B0ebgb6NfaiX2Mv/tbDytHsUradKp+9TU4vprKVCSnZpaRkl/LyV3m4GaBLkAcDGnsxoIkXt4Z64eNevTsyiXOoUqAVERFxtGV9AhxdgjiAwWCgrcmDtiYPHu7kT0ZBGR+eKWT76UJ2nC0ir5KFtxYrfHGxhC8ulvDcl7l4GaFniCcDmnhzexMvugR54O6mgFsXKNCKiIiIywjxMTI+yo/xUX4UlVl594sTHDeGsPNcIV9cLKGskunbojLYdaGYXReKWfAFNPQw0Ce8fPZ2QGMv2pncq31PfakdCrQiIuISzueXr6PUTT9yhZfRQHeThbFRDZnbrSGXiy3svlDEzvNFfHquqNJtwQAul1jZdrp8KQNAoJcbEQ2MhPm4EeZjJMz36p/Dfb875mPEW8sWnI4CrYiIuIT26y8AYJ7Y1MGViLNq6OnG0Egf257F6QVlfHq+iJ3nivjkfBGnc69/cxnApSILl4pu/BCHRp4Gwn2MhPq4Ee5r/C7oun0XgMvDbxM/I/4euiGttijQioiISwj3UTiQHyfUx8joVr6MbuWL1Wrl25wydn4XcD89X0RmFcLrtWQXW8kuLiUlu/J2rfyNdAvxpGuwJ92CPegU6IGfQm6NUKAVERGX8PXYxo4uQVyYwWCgZUN3WjZ0Z0JbPyxWK4ezStl5rpBPzxex+0JxpTeY3YwTOWWcyClgw4kCANwM0M7kTrdgz+9+eRAT4IGnUUsYfioFWhEREal33AwGOgZ60DHQgz928KfUYuVsXhlpBWWkFVhIyy/jQoGF9IIy25/T8stIL7Rgucnca7HCkaxSjmSV8uax8n1zvYzQIcCDbsGedA32oFuIJ1EN3TFq94UfRYFWRERE6j13NwPN/d1p7l95NCqzWMkssnAhv4z0AgsXCspIy7d8F4TL/3yhoIxTuWVVCr5FZbD/Ygn7L5bYjjVwN9A52IOuQeWzuD1CPYlooMhWGfWOiIi4hAEJ6QDsHB7q4EqkPjO6GQj1MRLqU/luG3klFg5dKt8D98DFYr7IKOZEJU88+77cUiu7LxSz+0Kx7djoVj4s7mUiQE8+uyYFWhERcQn/yyy5cSMRJ+Hn4cZtYV7cFuZlO2YusnAws7j8YQ8ZxRy4WMLZ/KqF3A0nCvj8QhEr+wUwoIl3TZXtshRoRUTEJXxyT4ijSxD5SUxebtzexJvbvxdIL+SXlc/gXpnJvVhy3a3DzuVbGPFBJn+8pQF/7d4QL91MZqNAKyIiLqFLsKejSxCpduG+Rru9c61WK6m5V0Puh6cLScm2f0DEi4dz2XGukJf7B3JLoIcjynY6WoghIiIi4iQMBgMt/N2JbenLUz0asWtEKI90bMAP52KPZJUy8L10Xjqci8VavduNuSIFWhERcQlPH7jM0wcuO7oMkVrlaTTwxM8a8d7QYJr52d+IVlQGjyZn88sPMzmXV7W1uHWVAq2IiLiERQdzWHQwx9FliDhEn3Avdo8M5VetfSqc++RcEb03pbH52wIHVOYcFGhFRMQlzOriz6wu/o4uQ8RhGnm6sbp/IK8OCKCRp/0iBHOxlfsTLzF5VxaXi2/ukb6uTIFWRERcwpyuDZnTtaGjyxBxuFGtfNk9IpR+4RVvlPzX8Xz6bk4nKa3IAZU5jgKtiIiIiItp1sCdzXcF89TPGuLxgzR3KreMYdsusmD/ZUpu9jm9LkaBVkREXMLBi8UcvFh844Yi9YSbwcBDHf3ZcU8o7U32O7FarLDkUA6Dt2ZwLLvuP5REgVZERFzC7VsyuH1LhqPLEHE6HQM92HFPKA/G+FU4d+BiCf03Z/Da13lY6/D2Xgq0IiLiEjoHedA5SJvIi1yLj7uBhbea2Dg4iHAf+3hXUGblz0lmxv4nk/SCurm9lwKtiIi4hJ3DQ9k5PNTRZYg4tYFNvfl8ZCj3NPeucO6DM0X03pTOh6cLHVBZzVKgFREREalDAr2N/PPngazoa6KBu/32XhcLLfzqP5ks+OIyZXXohjEFWhEREZE6xmAwMD7Kj10jQukZUnF7ryX/y2HUR5lcLKwbSxAUaEVExCW0e/s87d4+7+gyRFxKy4buvH93MI929cdoP1nLJ+eKGLA5g33prr97iAKtiIi4hAsFFi4U1L8nIIn8VO5uBmZ2acjmu4IJ/cENY2fzy7h7WwarjuS69C4ICrQiIuISvhoTzldjwh1dhojL6hvuxafDQ7ktzH4JQokFZu3N5oGdWeSWuOaHRgVaERFxCY19jTT2NTq6DBGXFu5rJOGuYB7q0KDCuXdOFjBoSwYpZtd7EIMCrYiIiEg94uFm4KkejXj954H4e9gvrE3JLmXglgzePZnvoOpujgKtiIi4hGm7s5i2O8vRZYjUGSNa+JB4TwgxP3hsbl6plYmfZDF7r5niMtdYV6tAKyIiLuH1o/m8ftS1Zo1EnF2bRh589IsQftXap8K5fxzJ457tFzmX5/xbeynQioiIS3i+t4nne5scXYZInePn4caqfgEsvc2E5w+S4d70YvonpLPznHM/XUyBVkREXMKEtn5MaOvn6DJE6iSDwcDv2vmx/e4QmvnZ33x5sdBC7IeZLD2Ug8VJt/ZSoBURERERALqFePLp8BDuaOpld9xihfn7LzPu40uYi5xvay8FWhERcQnbThWw7VSBo8sQqfMCvY3E3xnEnK7+/ODhYnxwupABCen8L9O5ni6mQCsiIi5h3MeXGPfxJUeXIVIvuBkMzOrSkA2Dgwjwso+1qbllDN6aweYLzrMvtAKtiIi4hCER3gyJ8HZ0GSL1yqCm3uwcHkq3YA+740VlsOgbT77NKXVQZfYUaEVExCWsvyOI9XcEOboMkXonsoE72+4O4YF29jdlTm9VQgt/9+u8qna5VKBdunQpJpOJGTNm2I5ZrVaefvpp2rVrR3h4OMOGDeOrr75yYJUiIiIidYuX0cCS20ys6h+Aj9HAmNY+xIY7x+wsuFCg3bdvH2vXruWWW26xO75s2TJWrFjBokWL2LFjByEhIcTGxpKTk+OgSkVERETqpjGtfUkcHsJzvU0YfnjHmAO5RKDNzs7mD3/4Ay+++CImk8l23Gq1snLlSh5++GFGjBhBTEwMK1euJDc3lw0bNjiuYBERqXamNWcxrTnr6DJE6r12Jg983Z0rQjpXNddxJbD279/f7nhqaippaWkMHDjQdszHx4fevXuzd+/e2i5TRERERBzAOVbyVuL111/nxIkTrF69usK5tLQ0AEJCQuyOh4SEcP78+eu+57Fjx6q3yBuo7e8nV6nvHUP97jh1ue/39S3/3Vmv0Vnrqg/U945Tm30fFRV13XNOHWiPHTvG/Pnz2b59Ox4eHjd+QRVV1iHV7dixY7X6/eQq9b1jqN8dR33vOOp7x1HfO44z9b1TLzlITk4mMzOTXr16ERQURFBQELt37+aVV14hKCiIwMBAADIyMuxel5GRQWhoqCNKFhEREZFa5tQztMOGDaNr1652x6ZOnUrr1q3585//TJs2bQgLCyMxMZFu3boBUFhYSFJSEvPnz3dEySIiIiJSy5w60JpMJrtdDQB8fX0JCAggJiYGgMmTJ7N06VKioqJo06YNS5Yswc/Pj9GjRzugYhERERGpbU4daKti2rRpFBQUMGPGDMxmM927d2fjxo34+/s7ujQRERERqQUuF2i3bt1q97XBYGDOnDnMmTPHQRWJiIiIiCM59U1hIiIiIiI3okArIiIiIi5NgVZEREREXJoCrYiIiIi4NAVaEREREXFpBrPZbHV0ESIiIiIiN0sztCIiIiLi0hRoRURERMSlKdCKiIiIiEtToBURERERl6ZAKyIiIiIuTYG2Ert372bs2LG0b98ek8nEunXr7M6np6czefJk2rVrR+PGjRk1ahTffPON7Xxqaiomk+mav1544QVbu6KiImbMmEGrVq1o0qQJY8eO5ezZs7V2nc6otvp+2LBhFc7/7ne/q7XrdEY/te8B0tLSiIuLIzo6msaNG9OnTx/i4+Pt2pjNZuLi4oiMjCQyMpK4uDjMZnNNX55Tq62+79ixY4VxP2/evJq+PKdWHX1/8uRJxo8fT+vWrYmIiGDChAmkp6fbtdG4r6i2+l7jvqKlS5fy85//nIiICFq3bs2YMWM4cuSIXRur1crTTz9Nu3btCA8PZ9iwYXz11Vd2baoyrg8fPszdd99NeHg47du3Z9GiRVit1bfRlgJtJfLy8oiJiWHhwoX4+PjYnbNarYwfP54TJ06wbt06Pv30UyIiIhgxYgR5eXkANGvWjJSUFLtfzz77LAaDgeHDh9vea86cOWzZsoVXX32V999/n5ycHMaMGUNZWVmtXq8zqa2+Bxg/frxdu+eee67WrtMZ/dS+B3jwwQc5evQob731FklJSYwdO5ZJkyaxe/duW5sHHniAQ4cOsWHDBjZs2MChQ4eYNGlSrV2nM6qtvgeYOXOm3bifPn16rVyjs/qpfZ+Xl0dsbCxWq5WEhAS2b99OcXExY8eOxWKx2N5L476i2up70Lj/oc8++4zf//73fPDBByQkJODu7s7IkSPJysqytVm2bBkrVqxg0aJF7Nixg5CQEGJjY8nJybG1udG4vnz5MrGxsYSGhrJjxw4WLlzI8uXLefHFF6vtWrQPbRU1bdqUZ555hvHjxwNw/Phxfvazn7Fr1y46duwIgMViITo6mscff5zf/va313yfkSNHYjAYePfddwHIzs6mTZs2rFixgl/96lcAnDlzho4dO7JhwwYGDRpUC1fn3Gqq76F8hjYmJobFixfX/IW4oJvt+6ZNm7Jo0SLuu+8+23t16NCBSZMm8dBDD5GSksKtt97K9u3b6dWrFwBJSUkMHTqUffv2ERUVVctX6nxqqu+hfKYqLi7O9rXYu5m+37FjB6NGjeLkyZOYTCag/N/3Fi1a8O6773L77bdr3FdBTfU9aNxXRW5uLpGRkaxbt46hQ4ditVpp164df/jDH2zhv6CggKioKJ566ikmTpxYpXH96quvMm/ePI4ePWr70LJ48WJee+01jhw5gsFg+Mm1a4b2JhUVFQHg7e1tO+bm5oaXlxdJSUnXfM23337Lzp07mTBhgu3YwYMHKSkpYeDAgbZjzZo1o23btuzdu7dmindx1dX3V7zzzju0atWKXr16MXfuXLtPnWKvqn3fq1cvNm3axKVLl7BYLGzdupXMzEwGDBgAQHJyMg0aNODWW2+1e42fn5/G/XVUV99fsXz5clq2bEnfvn1ZsmQJxcXFtXMhLqgqfV9UVITBYMDLy8vWxtvbGzc3N1sbjfsfr7r6/gqN+8rl5uZisVhsHwxSU1NJS0uzyyg+Pj707t3bNmarMq6Tk5O57bbb7GbgBw0axPnz50lNTa2W2hVob1J0dDTNmjVj/vz5ZGVlUVxczPPPP8/Zs2dJS0u75mv++c9/EhwczN133207lp6ejtFoJCgoyK5tSEhIhfU/Uq66+h7g3nvv5eWXX2bLli3MmDGDhISE687wStX7fs2aNRgMBlq1akVoaChxcXG88sordOrUCSgf90FBQXafyg0GA8HBwRr311FdfQ8wadIkXnnlFbZs2UJcXBwvvfQSf/nLXxxxWS6hKn3fo0cPGjRowOOPP05eXh55eXnMnTuXsrIyWxuN+x+vuvoeNO6rYvbs2XTs2JGePXsC2PovJCTErt33M0pVxnV6evo13+PKueqgQHuTPDw8ePPNNzl58iQtW7akcePG7Nq1izvvvBM3t4rdWlpayrp16xg3bhweHh4OqLjuqM6+nzBhAoMGDeKWW25h1KhRrFmzhsTERA4ePFhLV+Naqtr3CxYsIDMzk82bN5OYmMhDDz3E5MmT+fLLLx1YvWurzr7/4x//SP/+/enQoQO//e1vefbZZ3njjTe4dOmSIy7N6VWl74ODg1m7di0fffQRzZo1IzIykuzsbDp37nzNf5ekaqqz7zXuK/foo4+yZ88e3njjDYxGo6PL+dHcHV2AK+vSpQufffYZ2dnZlJSUEBwczKBBg+jatWuFttu2bSMtLa3C7F9oaChlZWVkZmYSHBxsO56RkcFtt91W49fgqqqj76+la9euGI1GTpw4QZcuXWqgctd3o74/efIkq1evtlvz1rFjR5KSkli9ejXLly8nNDSUzMxMrFar7VO91Wrl4sWLhIaGOuzanF119P21dO/eHYATJ04QGBhYOxfjYqryb87AgQM5ePAgmZmZGI1GTCYT0dHRtGjRAkDj/iZVR99fi8b9VXPmzGHjxo1s2bLFrs/CwsKA8kwSERFhO56RkWEbs1UZ16GhoWRkZNh9zytfV9fY18fGatCoUSOCg4P55ptvOHDgQIUfa0P5j7z79OlDmzZt7I536dIFDw8PEhMTbcfOnj1rW2QtlfspfX8thw8fpqyszPaXWK7ven2fn58PUOETvtFotN1x3LNnT3Jzc0lOTradT05OJi8vT+O+Cn5K31/Lldlbjfsbq8q/OUFBQZhMJnbu3ElGRgZDhw4FNO5/qp/S99eicV9u1qxZvPPOOyQkJBAdHW13rnnz5oSFhdlllMLCQpKSkmxjtirjumfPniQlJVFYWGhrk5iYSOPGjWnevHm1XIdmaCuRm5vLiRMngPK7Ks+cOcOhQ4cICAggIiKCTZs2ERgYSGRkJIcPH2b27NkMGzbMbvE0wOnTp/n444/5xz/+UeF7NGrUiN/85jc88cQThISEEBAQwGOPPcYtt9xiuzOzPqqNvj958iTx8fEMHjyYwMBAUlJSmDt3Lp06dbLdqVkf/dS+j46OplWrVvzlL39hwYIFBAYG8t5775GYmMhbb70FQNu2bbnjjjt45JFHeP755wF45JFHGDJkSL2+07s2+j45OZl9+/bRr18/GjZsyIEDB3j00UcZOnSo3QxMfVMd/+a8+eabREdHExISQnJyMrNnz2bKlCm2Ma1xf2210fca99c2ffp01q9fz5tvvonJZLKtmfXz86NBgwYYDAYmT57M0qVLiYqKok2bNixZsgQ/Pz9Gjx4NVG1cjx49mkWLFjFlyhSmT5/O8ePHef7555k5c2a17HAA2rarUrt27eKee+6pcHzcuHGsXLmSf/zjHyxfvpz09HTCwsIYO3YsM2fOxNPT06793//+d1avXs3XX39td6fmFUVFRcydO5cNGzZQWFhI//79efbZZ2nWrFmNXZuzq42+P3PmDHFxcXz11Vfk5eXRtGlTBg8ezOzZswkICKjR63Nm1dH333zzDfPmzWPPnj3k5eXRsmVLpk6dyq9//WtbG7PZzMyZM9m2bRsAQ4cO5ZlnnrHdXVsf1UbfHzx4kOnTp3P06FGKi4uJiIjgl7/8JdOmTcPX17fWrtXZVEffz5s3j7feeousrCwiIyOZOHEiU6dOtfsPW+O+otroe437a7veuJs1axZz5swBypcPLFy4kLVr12I2m+nevTtLliwhJibG1r4q4/rw4cNMnz6dL774ApPJxMSJE5k1a5YCrYiIiIgIaA2tiIiIiLg4BVoRERERcWkKtCIiIiLi0hRoRURERMSlKdCKiIiIiEtToBURERERl6ZAKyIiIiIuTYFWRERERFyaAq2IiIiIuDQFWhERsSkrK6OoqMjRZYiI/CgKtCIiDpaYmIjJZCIhIaHCuffffx+TycRHH30EwPHjx5kwYQItW7YkLCyMfv36sXnzZrvXZGVl8de//pXevXvTrFkzmjZtyrBhw/j888/t2qWmpmIymXjuuedYvXo13bp1IzQ0lOTk5Jq7WBGRGuDu6AJEROq7AQMG0KRJE+Lj4xk+fLjdufj4eMLCwhg4cCApKSkMHjyYsLAwpk2bhp+fH++99x73338/q1atYsyYMQB8++23bN68mdjYWFq0aEF2djZvvPEGI0eOZMeOHXTo0MHue6xfv568vDwmTJhAgwYNCA8Pr7VrFxGpDgaz2Wx1dBEiIvXdvHnzWLlyJSkpKZhMJgAuX75MdHQ0v/vd7/j73/9ObGws58+fJzExER8fH9trY2NjSUlJ4fDhwxgMBoqKivDw8MDN7eoP4cxmMz169OCuu+5i+fLlQPkMbefOnfHz82P//v0KsiLisrTkQETECYwbN46ioiLeffdd27HNmzdTWFjImDFjyMrK4pNPPmHkyJHk5+eTmZlp+zVo0CDOnTvH8ePHAfDy8rKF2cLCQi5dukRZWRndunXj4MGDFb73sGHDFGZFxKVpyYGIiBNo27Yt3bp1Iz4+nokTJwLlyw3at29P586d2b9/P1arlYULF7Jw4cJrvkdGRgZRUVFYLBaWLVvG2rVrSU1NtWvTvHnzCq9r0aJFtV+PiEhtUqAVEXESY8eOZdasWaSmpuLu7s7u3bt5/PHHAbBYLABMmTKFwYMHX/P1MTExACxdupQFCxYwbtw45s6dS2BgIEajkaVLl3Ly5MkKr/v+8gUREVekQCsi4iRGjx7N3LlziY+Px9PTE4B7770XuDqL6u7uzu23317p+2zatIm+ffuycuVKu+NPP/10tdcsIuIMtIZWRMRJBAYGMnjwYOLj44mPj6dfv340bdoUgJCQEPr168frr7/OuXPnKrz24sWLtj8bjUasVvv7fffu3avtuESkztIMrYiIExk7diz33XcfAC+99JLduaVLlzJkyBD69OnD/fffT8uWLcnIyOC///0vKSkpHDhwAIChQ4eycOFCJk2aRO/evfnmm29Yu3Yt7dq1Izc3t9avSUSkpinQiog4kSFDhhAUFERBQUGFPWmjoqJITExk0aJFvP3222RmZhIcHEyHDh147LHHbO3+/Oc/U1BQwL///W82b95M+/btee2113jnnXf47LPPavuSRERqnPahFRFxImVlZcTExNC/f39efvllR5cjIuIStIZWRMSJbNu2jbS0NMaNG+foUkREXIZmaEVEnMB///tfjhw5wuLFi/H392f37t0YDAZHlyUi4hI0Qysi4gReffVVHnnkEQICAli9erXCrIjIj6AZWhERERFxaZqhFRERERGXpkArIiIiIi5NgVZEREREXJoCrYiIiIi4NAVaEREREXFpCrQiIiIi4tL+HxkChddRHAHjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "(cigar\n",
    " .assign(california = np.where(cigar[\"california\"], \"California\", \"Other States\"))\n",
    " .groupby([\"year\", \"california\"])\n",
    " [\"cigsale\"]\n",
    " .mean()\n",
    " .reset_index()\n",
    " .pivot(\"year\", \"california\", \"cigsale\")\n",
    " .plot(ax=ax, figsize=(10,5)))\n",
    "\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.ylabel(\"Cigarette Sales Trend\")\n",
    "plt.title(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.legend();  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the time for which we have data, people in California apparently bought less cigarettes than the national average. Also, it appears to be a decreasing movement in cigarette consumption after the 80s. It looks like after Proposition 99 the decreasing trend accelerated for California, compared to other states, but we can't say that for sure. It is just a guess that we have by examining the plot. \n",
    "\n",
    "To answer the question of whether Proposition 99 had an effect on cigarette consumption, we will use the pre-intervention period to build a synthetic control. We will combine the other states to **build a fake state that resembles very closely the trend of California**. Then, we will see how this synthetic control behaves after the intervention. \n",
    "\n",
    "## We have Time\n",
    "\n",
    "To make matters a little bit more formal, suppose that we have $J+1$ units. Without loss of generality, assume that unit 1 is the unit that gets affected by an intervention. Units $j=2,...,J+1$ are a collection of untreated units that we will refer to as the \"donor pool\". Also assume that the data we have span T time periods, with $T_0$ periods before the intervention. For each unit j and each time t, we observe the outcome $Y_{jt}$. For each unit j and period t, define $Y^N_{jt}$ as the potential outcome without intervention and $Y^I_{jt}$, the potential outcome with intervention. Then, the effect for the treated unit $j=1$ at time t, for $t>T_0$ is defined as \n",
    "\n",
    "$\n",
    "\\tau_{1t} = Y^I_{1t} - Y^N_{1t}\n",
    "$\n",
    "\n",
    "Since unit $j=1$ is the treated one, $Y^I_{1t}$ is factual but $Y^N_{1t}$ is not. The challenge then becomes how do we estimate $Y^N_{1t}$. Notice how the treatment effect is defined for each period, which means it can change in time. It doesn't need to be instantaneous. It can accumulate or dissipate. To put it in a picture, the problem of estimating the treatment effect boils down to the problem of **estimating what would have happened to the outcome of unit $j=1$ if it had not been treated**.\n",
    "\n",
    "![img](data/img/synth-control/synth_img.png)\n",
    "\n",
    "To estimate $Y^N_{1t}$, we remember that a combination of units in the donor pool may approximate the characteristics of the treated unit much better than any untreated unit alone. Thus, a synthetic control is defined as a weighted average of the units in the control pool. Given the weights $\\pmb{W}=(w_2, ..., w_{J+1})$, the synthetic control estimate of $Y^N_{1t}$ is\n",
    "\n",
    "$\n",
    "\\hat{Y}^N_{1t} = \\sum^{J+1}_{j=2} w_j Y_{jt}\n",
    "$\n",
    "\n",
    "If all this math makes your head hurt, you are not alone. But don't worry, we have lots of examples to make it more intuitive. For once, I like to think about synthetic control as an upside down way of doing regression. As we know, linear regression is also a way of getting the prediction as a weighted average of the variables. Now, think about those regressions like the one in the diff-in-diff example where each variable is a dummy for a time period. In this case, regression can be represented as the following matrix multiplication\n",
    "\n",
    "![img](data/img/synth-control/regr_time.png)\n",
    "\n",
    "On the synthetic control case, we don't have lots of units, but we do have lots of time periods. So what we do is flip the input matrix around. Then, the units become the \"variables\" and we represent the outcome as a weighted average of the units, like in the following matrix multiplication.\n",
    "\n",
    "![img](data/img/synth-control/regr_space.png)\n",
    "\n",
    "If we have more than one feature per time period, we can pile up the features like this. The important thing is to make it so that the regression is trying to \"predict\" the treated unit 1 by using the other units. This way, we can choose the weights in some optimal way to achieve this proximity we want. We can even scale features differently to give different importance to them.\n",
    "\n",
    "![img](./data/img/synth-control/regr_space_x.png)\n",
    "\n",
    "So, if synthetic control can be viewed as a linear regression, it also means that we can estimate its weights with OLS right? Yup! In fact, let's do this now.\n",
    "\n",
    "\n",
    "## Synthetic Control as Linear Regression\n",
    "\n",
    "![img](./data/img/synth-control/allways.png)\n",
    "\n",
    "To estimate the treatment effect with synthetic control, we will try to build a \"fake unit\" that resembles the treated unit before the intervention period. Then, we will see how this \"fake unit\" behaves after the intervention. The difference between the synthetic control and the unit that it mimics is the treatment effect.\n",
    "\n",
    "To do this with linear regression, we will find the weight using OLS. We will minimise the square distance between the weighted average of the units in the donor pool and the treated unit for the pre-intervention period.\n",
    "\n",
    "To do so, the first thing we need is to convert the units (in our case, the states) into the columns and the time into the rows. Since we have 2 features, `cigsale` and `retprice`, we will pile them on top of each other like we did in the picture above. We will build a synthetic control that looks a lot like California in the pre intervention period and see how it would behave in the post intervention period. For this reason, it is important that we select only the pre-intervention period. Here, the features seem to be on a similar scale, so we won't do anything to them. If features are in different scales, one in the thousands and another in the decimals, the bigger feature will be the most important when minimizing the difference. To avoid this, it's important to scale them first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">cigsale</th>\n",
       "      <th>1970</th>\n",
       "      <td>89.800003</td>\n",
       "      <td>100.300003</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>106.400002</td>\n",
       "      <td>132.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>95.400002</td>\n",
       "      <td>104.099998</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>111.500000</td>\n",
       "      <td>105.400002</td>\n",
       "      <td>131.699997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>101.099998</td>\n",
       "      <td>103.900002</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>108.800003</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>102.900002</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>...</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>109.500000</td>\n",
       "      <td>141.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>108.199997</td>\n",
       "      <td>109.699997</td>\n",
       "      <td>126.699997</td>\n",
       "      <td>...</td>\n",
       "      <td>119.900002</td>\n",
       "      <td>111.800003</td>\n",
       "      <td>145.800003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "state                 1           2           3   ...          37          38  \\\n",
       "        year                                      ...                           \n",
       "cigsale 1970   89.800003  100.300003  123.000000  ...  114.500000  106.400002   \n",
       "        1971   95.400002  104.099998  121.000000  ...  111.500000  105.400002   \n",
       "        1972  101.099998  103.900002  123.500000  ...  117.500000  108.800003   \n",
       "        1973  102.900002  108.000000  124.400002  ...  116.599998  109.500000   \n",
       "        1974  108.199997  109.699997  126.699997  ...  119.900002  111.800003   \n",
       "\n",
       "state                 39  \n",
       "        year              \n",
       "cigsale 1970  132.199997  \n",
       "        1971  131.699997  \n",
       "        1972  140.000000  \n",
       "        1973  141.199997  \n",
       "        1974  145.800003  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [\"cigsale\", \"retprice\"]\n",
    "\n",
    "inverted = (cigar.query(\"~after_treatment\") # filter pre-intervention period\n",
    "            .pivot(index='state', columns=\"year\")[features] # make one column per year and one row per state\n",
    "            .T) # flip the table to have one column per state\n",
    "\n",
    "inverted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can define our Y variable as the state of California and the X as the other states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = inverted[3].values # state of california\n",
    "X = inverted.drop(columns=3).values  # other states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we run a regression. Having an intercept is equivalent to adding another state where every row is 1. You can do that, but I think it's more complicated and I'll just leave it out. The regression will return the set of weights that minimize the square difference between the treated unit and the units in the donor pool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.436, -1.038,  0.679,  0.078,  0.339,  1.213,  0.143,  0.555,\n",
       "       -0.295,  0.052, -0.529,  1.235, -0.549,  0.437, -0.023, -0.266,\n",
       "       -0.25 , -0.667, -0.106, -0.145,  0.109,  0.242, -0.328,  0.594,\n",
       "        0.243, -0.171, -0.02 ,  0.14 , -0.811,  0.362,  0.519, -0.304,\n",
       "        0.805, -0.318, -1.246,  0.773, -0.055, -0.032])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "weights_lr = LinearRegression(fit_intercept=False).fit(X, y).coef_\n",
    "weights_lr.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These weights show us how to build the synthetic control. We will multiply the outcome of state 1 by -0.436, of state 2 by -1.038, of state 4 by 0.679 and so on. We can achieve this with a dot product between the matrix from the states in the pool and the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "calif_synth_lr = (cigar.query(\"~california\")\n",
    "                  .pivot(index='year', columns=\"state\")[\"cigsale\"]\n",
    "                  .values.dot(weights_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our synthetic control, we can plot it with the outcome variable of the State of California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"], label=\"California\")\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], calif_synth_lr, label=\"Synthetic Control\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK… Something seems off. What grabs your attention in this picture? First, after the intervention, the synthetic control has more cigarette sales than California. This is an indicative that the intervention was successful in lowering cigarette demand. Second, notice how the pre-intervention period is fitted perfectly. The synthetic control is able to match the state of California exactly. This is a sign that our synthetic control model is probably overfitting the data. Another sign is the huge variance on the outcome variable of the synthetic control after the intervention. Notice how it doesnt follow smooth patterns. Instead, it goes up and down and up and down. \n",
    "\n",
    "![img](./data/img/synth-control/out-of-sample.png)\n",
    "\n",
    "If we think about why this is happening, remember that we have 38 states in our donor pool. So our linear regression has 38 parameters to play with in order to make the pretreatment pool match the treatment as close as it can. This is the case where, even if T is large, N is also large, which gives too much flexibility to our linear regression model. If you are familiar with regularized models, know that you could use Ridge or Lasso regression to fix this. Here, we will look at another more traditional way to avoid overfitting.\n",
    "\n",
    "## Don't Extrapolate\n",
    "\n",
    "Suppose you have data like in this table below and are asked to build a synthetic control to reproduce the treated unit using any linear combination of the control units.\n",
    "\n",
    "|unit|sales|price|\n",
    "|--|--|--|\n",
    "|control 1|8|8|\n",
    "|control 2|8|4|\n",
    "|control 3|4|5|\n",
    "|treated  |2|10|\n",
    "\n",
    "Since there are 3 units and only 2 attributes to match, there are multiple exact solutions to this problem, but a nice one is multiplying the first control by 2.25, multiplying the second by -2 and adding both. Notice how the second multiplication creates a fake unit with sales of -16 and price of -8. This multiplication is extrapolating the control 2 unit to a region of the data that doesn't make a lot of sense, since negative price and sales are almost impossible. The first multiplication is also an extrapolation, since it takes the first unit to a region where sales and price are 18. These numbers are much higher than anything we have in our data, hence the extrapolation.\n",
    "\n",
    "This is what regression is doing when we ask it to create a synthetic control. Extrapolation is not technically wrong, but it's dangerous in practice. We are making assumptions that the data we have never seen behaves like the data that we have. \n",
    "\n",
    "One way to play safer is to constrain our synthetic control to only do interpolation. To do so, we will restrict the weights to be positive and sum up to one. Now, the synthetic control will be a convex combination of the units in the donor pool. When doing interpolation, we will project the treated unit in the convex hull defined by the untreated unit, much like in the picture below.\n",
    "\n",
    "![img](data/img/synth-control/extrapolation.png)\n",
    "\n",
    "Notice two things here. First, interpolation won't be able to create a perfect match of the treated unit in this case. This is because the treated is the unit with the smallest number of sales and the highest price. Convex combinations can only replicate exactly features that are in between the control units. Another thing to notice is that interpolation is sparse. We will project the treated unit on a wall of the convex hull and this wall is defined only by a few units. For this reason, interpolation will assign weight zero to many of the units. \n",
    "\n",
    "This is the general idea, now let's formalize it a little bit. The synthetic control is still defined as \n",
    "\n",
    "$\n",
    "\\hat{Y}^N_{jt} = \\sum^{J+1}_{j=2} w_j Y_{jt}\n",
    "$\n",
    "\n",
    "but now, we will use weights $\\pmb{W}=(w_2, ..., w_{J+1})$ that minimises\n",
    "\n",
    "$\n",
    "||\\pmb{X}_1 - \\pmb{X}_0 \\pmb{W}|| = \\bigg(\\sum^k_{h=1}v_h \\bigg(X_{h1} - \\sum^{J+1}_{j=2} w_j X_{hj} \\bigg)^2 \\bigg)^{\\frac{1}{2}}\n",
    "$\n",
    "\n",
    "subject to the restriction that $w_2, ..., w_{J+1}$ are positive and sum to one. Notice that $v_h$ reflect the importance of each variable when minimising the difference between the treated and the synthetic control. Different $v$s would give different optimal weights. One way to choose $V$ is to make it so that each variable has mean zero and unit variance. A more complex way is to choose $V$ in such a way that variables that help to predict $Y$ better get higher importance. Since we want to keep the code simple, we will simply give the same importance for each variable.\n",
    "\n",
    "To implement this, first, define the above loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from operator import add\n",
    "from toolz import reduce, partial\n",
    "\n",
    "def loss_w(W, X, y) -> float:\n",
    "    return np.sqrt(np.mean((y - X.dot(W))**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are using the same importance for every feature, we don't need to worry about $v$.\n",
    "\n",
    "Now, to get the optimal weights, we will use the quadratic programming optimisation of scipy. We will constrain the weights to sum up to 1 with \n",
    "\n",
    "```python \n",
    "lambda x: np.sum(x) - 1\n",
    "```\n",
    "\n",
    "Also, we will set optimization bounds to be between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import fmin_slsqp\n",
    "\n",
    "def get_w(X, y):\n",
    "    \n",
    "    w_start = [1/X.shape[1]]*X.shape[1]\n",
    "\n",
    "    weights = fmin_slsqp(partial(loss_w, X=X, y=y),\n",
    "                         np.array(w_start),\n",
    "                         f_eqcons=lambda x: np.sum(x) - 1,\n",
    "                         bounds=[(0.0, 1.0)]*len(w_start),\n",
    "                         disp=False)\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this implemented, let's get the weights that define the synthetic control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum: 1.000000000000424\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.    , 0.    , 0.    , 0.0852, 0.    , 0.    , 0.    , 0.    ,\n",
       "       0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "       0.    , 0.    , 0.    , 0.113 , 0.1051, 0.4566, 0.    , 0.    ,\n",
       "       0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "       0.2401, 0.    , 0.    , 0.    , 0.    , 0.    ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calif_weights = get_w(X, y)\n",
    "print(\"Sum:\", calif_weights.sum())\n",
    "np.round(calif_weights, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this weight, we are multiplying states 1,2, and 3 by zero, state 4 by 0.0852 and\n",
    "so on. Notice how the weights are sparse, exactly as we've predicted. Also, all weights sum to one and are between 0 and 1, satisfying our convex combination constraint.\n",
    "\n",
    "Now, to get the synthetic control, we can multiply those weights by the states exactly as we did before with the regression weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "calif_synth = cigar.query(\"~california\").pivot(index='year', columns=\"state\")[\"cigsale\"].values.dot(calif_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the outcome of the synthetic control now, we get a much smoother trend. Also notice that, in the pre intervention period, the synthetic control doesn't reproduce the treated exactly anymore. This is a good sign, as it indicates that we are not overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"], label=\"California\")\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], calif_synth, label=\"Synthetic Control\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.ylabel(\"Per-capita cigarette sales (in packs)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the synthetic control at hand, we can estimate the treatment effect as the gap between treated and the synthetic control outcomes.\n",
    "\n",
    "$\n",
    "\\tau_{1t} = Y^I_{jt} - Y^N_{jt}\n",
    "$\n",
    "\n",
    "In our particular case, the effect gets bigger and bigger as time passes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"] - calif_synth,\n",
    "         label=\"California Effect\")\n",
    "plt.vlines(x=1988, ymin=-30, ymax=7, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=2)\n",
    "plt.title(\"State - Synthetic Across Time\")\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the year 2000, it looks like Proposition 99 has reduced the sales in cigarettes by 25 packs. That is very cool and all, but something you might be asking yourself is: how can I know if this is statistically significant?\n",
    "\n",
    "## Making Inference\n",
    "\n",
    "Since our sample size is very small (39), we will have to be a bit smarter when figuring out if our result is statistically significant and not just due to random luck. Here, we will use the idea of Fisher's Exact Test. Its intuition is very simple. We permute the treated and control exhaustively. Since we only have one treated unit, this would mean that, for each unit, we pretend it is the treated while the others are the control. \n",
    "\n",
    "|iteration|1|2|...|39|\n",
    "|----|-|-|-|-|\n",
    "|1|treated|0|0|0|\n",
    "|2|0|treated|0|0|\n",
    "|...|0|0|0|0|0|0|\n",
    "|39|0|0|0|treated|\n",
    "\n",
    "In the end, we will have one synthetic control and effect estimates for each state. So what this does is it pretends that the treatment actually happened for another state, not California, and see what would have been the estimated effect for this treatment that didn't happen. Then, we see if the treatment in California is sufficiently larger when compared to the other fake treatment. The idea is that for states that weren't actually treated, once we pretend they were, we won't be able to find any significant treatment effect. \n",
    "\n",
    "To implement this, I've built this function that takes as input a state and estimate the synthetic control for that state. This function returns a data frame with one column for the state, one for the year, one for the outcome `cigsale` and the synthetic outcome for that state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def synthetic_control(state: int, data: pd.DataFrame) -> np.array:\n",
    "    \n",
    "    features = [\"cigsale\", \"retprice\"]\n",
    "    \n",
    "    inverted = (data.query(\"~after_treatment\")\n",
    "                .pivot(index='state', columns=\"year\")[features]\n",
    "                .T)\n",
    "    \n",
    "    y = inverted[state].values # treated\n",
    "    X = inverted.drop(columns=state).values # donor pool\n",
    "\n",
    "    weights = get_w(X, y)\n",
    "    synthetic = (data.query(f\"~(state=={state})\")\n",
    "                 .pivot(index='year', columns=\"state\")[\"cigsale\"]\n",
    "                 .values.dot(weights))\n",
    "\n",
    "    return (data\n",
    "            .query(f\"state=={state}\")[[\"state\", \"year\", \"cigsale\", \"after_treatment\"]]\n",
    "            .assign(synthetic=synthetic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the result of it when we apply it to the first state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>after_treatment</th>\n",
       "      <th>synthetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>False</td>\n",
       "      <td>95.029419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.400002</td>\n",
       "      <td>False</td>\n",
       "      <td>99.118199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>False</td>\n",
       "      <td>101.881329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>False</td>\n",
       "      <td>103.938655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>108.199997</td>\n",
       "      <td>False</td>\n",
       "      <td>107.038474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  year     cigsale  after_treatment   synthetic\n",
       "0      1  1970   89.800003            False   95.029419\n",
       "1      1  1971   95.400002            False   99.118199\n",
       "2      1  1972  101.099998            False  101.881329\n",
       "3      1  1973  102.900002            False  103.938655\n",
       "4      1  1974  108.199997            False  107.038474"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_control(1, cigar).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the result for all the state, we parallelize the computation across 8 processes. If your computer has more or less cores, you can use a different number. This code will return a list of data frames like the one above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "\n",
    "control_pool = cigar[\"state\"].unique()\n",
    "\n",
    "parallel_fn = delayed(partial(synthetic_control, data=cigar))\n",
    "\n",
    "synthetic_states = Parallel(n_jobs=8)(parallel_fn(state) for state in control_pool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>after_treatment</th>\n",
       "      <th>synthetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>False</td>\n",
       "      <td>95.029419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.400002</td>\n",
       "      <td>False</td>\n",
       "      <td>99.118199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>False</td>\n",
       "      <td>101.881329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>False</td>\n",
       "      <td>103.938655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>108.199997</td>\n",
       "      <td>False</td>\n",
       "      <td>107.038474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  year     cigsale  after_treatment   synthetic\n",
       "0      1  1970   89.800003            False   95.029419\n",
       "1      1  1971   95.400002            False   99.118199\n",
       "2      1  1972  101.099998            False  101.881329\n",
       "3      1  1973  102.900002            False  103.938655\n",
       "4      1  1974  108.199997            False  107.038474"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_states[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the synthetic control for all the states, we can estimate the gap between the synthetic and the true state for all states. For California, this is the treatment effect. For the other states, this is like a placebo effect, where we estimate the synthetic control treatment effect where the treatment didn't actually happen. If we plot all the placebo effects along with the California treatment effect, we get the following figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "for state in synthetic_states:\n",
    "    plt.plot(state[\"year\"], state[\"cigsale\"] - state[\"synthetic\"], color=\"C5\",alpha=0.4)\n",
    "\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"] - calif_synth,\n",
    "        label=\"California\");\n",
    "\n",
    "plt.vlines(x=1988, ymin=-50, ymax=120, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=3)\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.title(\"State - Synthetic Across Time\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two aspects of this figure jump to the eyes. First, we can see that the variance after the intervention is higher than the variance before the intervention. This is expected, since the synthetic control is designed to minimize the difference in the pre-intervention period. Another interesting aspect is that there are some units we can't fit very well even in the pre-intervention period. This is also to be expected. For example, if some states have very high cigarette consumption, no convex combination of the other states will ever match them. \n",
    "\n",
    "Since those units are so poorly fit, it is a good idea to remove them from the analysis. One way to do it objectively is to set a threshold for pre-intervention error \n",
    "\n",
    "$\n",
    "MSE = \\frac{1}{N}\\sum\\bigg(Y_t - \\hat{Y}^{Synth}_t\\bigg)^2\n",
    "$\n",
    "\n",
    "and remove those units with high error. If we proceed like this and plot the same figure, this is what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pre_treatment_error(state):\n",
    "    pre_treat_error = (state.query(\"~after_treatment\")[\"cigsale\"] \n",
    "                       - state.query(\"~after_treatment\")[\"synthetic\"]) ** 2\n",
    "    return pre_treat_error.mean()\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "for state in synthetic_states:\n",
    "    \n",
    "    # remove units with mean error above 80.\n",
    "    if pre_treatment_error(state) < 80:\n",
    "        plt.plot(state[\"year\"], state[\"cigsale\"] - state[\"synthetic\"], color=\"C5\",alpha=0.4)\n",
    "\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"] - calif_synth,\n",
    "        label=\"California\");\n",
    "\n",
    "plt.vlines(x=1988, ymin=-50, ymax=120, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=3)\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.title(\"Distribution of Effects\")\n",
    "plt.title(\"State - Synthetic Across Time (Large Pre-Treatment Errors Removed)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the noise, we can see how extreme of a value is the effect in the state of California. This image shows us that if we pretend the treatment had happened to any other state, we would almost never get an effect so extreme as the one we got with California.\n",
    "\n",
    "This picture alone is a form of inference, but we can also derive a P-value from these results. All we have to do is see how many times the effects that we've got is below the effect of California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "California Treatment Effect for the Year 2000: -24.83015975607075\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  5.79715887,   0.89458999, -24.83015976,  -7.16628121,\n",
       "       -10.92204855,  37.1164056 , -15.06971721,  -0.49805125,\n",
       "       -18.45795062,  21.13366447,  12.57782745,  -1.47547826,\n",
       "        10.49627373, -11.67012352,   4.29850832,   8.04811402,\n",
       "        14.023224  ,   8.25002775,   0.32576354,  -8.40826871,\n",
       "        -2.12402707,  -7.42865061,   2.96157551,  24.10478137,\n",
       "         4.25211766, -17.75844568,   7.93334017,   2.81640128,\n",
       "        12.64955962, -17.47677514, -25.16040949, -12.26469139,\n",
       "        24.69067386,  10.36299584,  -8.59880329])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calif_number = 3\n",
    "\n",
    "effects = [state.query(\"year==2000\").iloc[0][\"cigsale\"] - state.query(\"year==2000\").iloc[0][\"synthetic\"]\n",
    "           for state in synthetic_states\n",
    "           if pre_treatment_error(state) < 80] # filter out noise\n",
    "\n",
    "calif_effect = cigar.query(\"california & year==2000\").iloc[0][\"cigsale\"] - calif_synth[-1] \n",
    "\n",
    "print(\"California Treatment Effect for the Year 2000:\", calif_effect)\n",
    "np.array(effects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if we want to test the one sided hypothesis that the effect in California is below zero, we can estimate the P-value as the proportion of times the effect in California is bigger than all the estimated effects.\n",
    "\n",
    "$\n",
    "PV=\\frac{1}{N}\\sum \\mathcal{1}\\{\\hat{\\tau}_{Calif} > \\hat{\\tau}_j\\}\n",
    "$\n",
    "\n",
    "As it turns out, the treatment effect for California in the year 2000 is -24.8, meaning that the intervention reduced the consumption of cigarettes by almost 25 packs. Out of all the other 34 placebo effects that we've estimated, only one is higher than the effect we found in California. So the p-value would be 1/35."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02857142857142857"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.array(effects) < calif_effect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can show the distribution of effects just to get a sense of how extreme the value of the effect in California really is. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, bins, _ = plt.hist(effects, bins=20, color=\"C5\", alpha=0.5);\n",
    "plt.hist([calif_effect], bins=bins, color=\"C0\", label=\"California\")\n",
    "plt.ylabel(\"Frquency\")\n",
    "plt.title(\"Distribution of Effects\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Ideas\n",
    "\n",
    "We've learned that if we only have aggregated level data on entities like cities or states, diff-in-diff won't allow us to do inference. Also, it has some other limitations, since it has to define a control unit and one single control unit might not be a very good representation of the counterfactual for the treated unit. \n",
    "\n",
    "To correct for that, we learned that we can build a synthetic control that combines multiple control units to make them resemble the treated unit. With this synthetic control, we were able to see what would have happened to our treated unit in the absence of a treatment. \n",
    "\n",
    "Finally, we saw how we could use Fisher's Exact Tests to do inference with synthetic control. Namely, we've pretended that the non-treated units were actually the treated and computed their effect. These were the placebo effects: the effects we would observe even without a treatment. We uses these to see if the treatment effect we've estimated was statistically significant. \n",
    "\n",
    "## References\n",
    "\n",
    "I like to think of this entire book as a tribute to Joshua Angrist, Alberto Abadie and Christopher Walters for their amazing Econometrics class. Most of the ideas here are taken from their classes at the American Economic Association. Watching them is what is keeping me sane during this tough year of 2020.\n",
    "* [Cross-Section Econometrics](https://www.aeaweb.org/conference/cont-ed/2017-webcasts)\n",
    "* [Mastering Mostly Harmless Econometrics](https://www.aeaweb.org/conference/cont-ed/2020-webcasts)\n",
    "\n",
    "I'll also like to reference the amazing books from Angrist. They have shown me that Econometrics, or 'Metrics as they call it, is not only extremely useful but also profoundly fun.\n",
    "\n",
    "* [Mostly Harmless Econometrics](https://www.mostlyharmlesseconometrics.com/)\n",
    "* [Mastering 'Metrics](https://www.masteringmetrics.com/)\n",
    "\n",
    "Other important reference is Miguel Hernan and Jamie Robins' book. It has been my trustworthy companion in the most thorny causal questions I had to answer.\n",
    "\n",
    "* [Causal Inference Book](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)\n",
    "\n",
    "Finally, I'd also like to compliment Scott Cunningham and his brilliant work mingling Causal Inference and Rap quotes:\n",
    "\n",
    "* [Causal Inference: The Mixtape](https://www.scunning.com/mixtape.html)\n",
    "\n",
    "![img](./data/img/poetry.png)\n",
    "\n",
    "## Contribute\n",
    "\n",
    "Causal Inference for the Brave and True is an open-source material on causal inference, the statistics of science. It uses only free software, based in Python. Its goal is to be accessible monetarily and intellectually.\n",
    "If you found this book valuable and you want to support it, please go to [Patreon](https://www.patreon.com/causal_inference_for_the_brave_and_true). If you are not ready to contribute financially, you can also help by fixing typos, suggesting edits or giving feedback on passages you didn't understand. Just go to the book's repository and [open an issue](https://github.com/matheusfacure/python-causality-handbook/issues). Finally, if you liked this content, please share it with others who might find it useful and give it a [star on GitHub](https://github.com/matheusfacure/python-causality-handbook/stargazers)."
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